Energy Conversion and Management 198 (2019) 111799
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Energy Conversion and Management
journal homepage: www.elsevier.com/locate/enconman
Review
A review of deep learning for renewable energy forecasting
a
a
Huaizhi Wang , Zhenxing Lei , Xian Zhang
a
b
c
b,⁎
c
, Bin Zhou , Jianchun Peng
T
a
College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong
Department of Electrical and Information Engineering, Hunan University, Changsha 410082, China
A R T I C LE I N FO
A B S T R A C T
Keywords:
Deep learning
Renewable energy
Deterministic forecasting
Probabilistic forecasting
Machine learning
As renewable energy becomes increasingly popular in the global electric energy grid, improving the accuracy of
renewable energy forecasting is critical to power system planning, management, and operations. However, this is
a challenging task due to the intermittent and chaotic nature of renewable energy data. To date, various methods
have been developed, including physical models, statistical methods, artificial intelligence techniques, and their
hybrids to improve the forecasting accuracy of renewable energy. Among them, deep learning, as a promising
type of machine learning capable for discovering the inherent nonlinear features and high-level invariant
structures in data, has been frequently reported in the literature. This paper provides a comprehensive and
extensive review of renewable energy forecasting methods based on deep learning to explore its effectiveness,
efficiency and application potential. We divide the existing deterministic and probabilistic forecasting methods
based on deep learning into four groups, namely deep belief network, stack auto-encoder, deep recurrent neural
network and others. We also dissect the feasible data preprocessing techniques and error post-correction
methods to improve the forecasting accuracy. Extensive analysis and discussion of various deep learning based
forecasting methods are given. Finally, we explore the current research activities, challenges and potential future
research directions in this topic.
1. Introduction
At present, fossil fuels are still the most important source of energy
in the world. Fossil fuels are hydrocarbons or their derivatives, including natural resources such as coal, oil and natural gas. Fossil fuels
take millions of years to form and the known viable reserves are being
depleted much faster than new fossil fuels are being made. At the same
time, fossil fuels emit greenhouse gases, which will accelerate climate
change such as global warming, thereby jeopardizing the environment
on which people depend. Therefore, in recent years, renewable energy
has attracted much attentions in the whole world. Renewable energy
refers to energy that can be recycled in nature, such as solar energy,
wind power, tidal energy and geothermal energy. Compared to fossil
fuels, renewable energy has at least two advantages. First, renewable
energy resources are abundant and renewable in the world, and they
are inexhaustible. Second, renewable energy is clean, green, and low
carbon, and thus is beneficial for the protection of environment.
Concretely, renewable energy can effectively reduce the emission of
sulfide (SO2), carbide (CO) and dust, thereby reducing the risk of atmospheric pollution and greenhouse effect. In addition, the use of renewable energy can reduce the exploitation of natural fossil fuels, and
⁎
achieve the purpose of protecting the ecological environment.
Moreover, renewable energy can reduce the discharge of solid waste to
reduce soil pollution. Renewable energy can also reduce the emission of
waste gas and waste liquid during use, thus achieving the purpose of
protecting water resources. Therefore, renewable energy has developed
very rapidly in recent years [1]. According to REN21’s 2017 report,
renewable energy accounts for 19.3% of global energy consumption
and 24.5% of electricity generation in 2016 [2]. Many countries, such
as the United States and China, have developed various regulatory
measures, incentives, and subsidies to encourage renewable energy
penetration [3].
Although renewable energy is considered to be the most promising
alternative to fossil fuels because it is clean, green and naturally replenished in a wide geographical area, it also brings unschedulable
uncertainty, which threatens the reliability and stability of energy
systems, especially with the large-scale integration of renewable energy. On the one hand, renewable energy exhibits strong volatility,
intermittent and randomness, which will undoubtedly increase the reserve capacity of the electric energy systems, thereby increasing the
cost of power generation. On the other hand, the use of renewable
energy involves a large number of power electronics, which reduces the
Corresponding author.
E-mail address: eexianzhang@outlook.com (X. Zhang).
https://doi.org/10.1016/j.enconman.2019.111799
Received 26 April 2019; Received in revised form 3 July 2019; Accepted 7 July 2019
0196-8904/ © 2019 Elsevier Ltd. All rights reserved.
Energy Conversion and Management 198 (2019) 111799
H. Wang, et al.
a denoising technique, multi-objective differential evolution algorithm
and fuzzy time series method was developed to balance forecasting
accuracy and parameter stability [27]. The hybrid method in [28]
based on wavelet packet decomposition, Elman neural networks and
boosting algorithm was built to investigate the big multi-step wind
speed forecasting performance. In addition, wavelet decomposition and
least square support vector machine were used to largely mitigate the
randomness and stochastics in wind energy [29]. Based on extreme
learning machines and empirical model decomposition, the authors in
[21] proposed a new machine learning framework for predicting significant wave heights in the eastern coastal area of Australia. This
framework can be seen as a relevant decision support framework and is
critical for designing reliable ocean energy converters.
However, the aforementioned methods for renewable energy forecasting generally adopt shallow models as their core of learning principles. The shallow models are neural networks with only one hidden
layer or no hidden layer. Shallow models were proposed in the 1980s to
learn statistical rules from a large number of training samples to predict
unknown events. Shallow models mainly include back propagation algorithm, support vector machine, Boosting and maximum entropy
methods. However, the training process of shallow models require a lot
of experience and skills. In addition, the theoretical analysis of shallow
models is also difficult. Therefore, shallow models have great limitations in practical applications. In other words, shallow models have at
least three main drawbacks: (1) Hand-engineered feature selection.
Shallow learning models require sufficient prior knowledge on the
problem domain to manually choose features from renewable energy
data [30]. The tedious feature selection process highly relies on personal experience and thus is actually unreliable, making shallow models
inappropriate for discovering the inherent nonlinear features and highlevel invariant structures in renewable data. (2) Limited generalization
capability. It has been proved that shallow models excel at approximation of smooth target function. However, renewable energy data is
intermittent, stochastic and highly-varying because of the noisy environment and chaotic nature of the weather system in earth, introducing non-smooth characteristics to the forecasting target function.
Therefore, shallow models with limited generation capability may not
suitable to learn the complex patterns in renewable energy data [31].
(3) Sample complexity. Shallow models work well when the training
dataset is relatively small. However, the widespread deployment of
environmental meters, remote sensors and other relevant technologies
drives us into the era of big data [32], showing an exponential upward
trend of the training data. Consequently, shallow models may suffer
network instability and parameters non-convergence due to ample renewable energy data. Therefore, the hand-engineered feature selection,
weak generalization capability and sample complexity inspire us to
rethink the renewable energy forecasting problem based on deep
learning architecture [33].
Deep learning, as a promising branch of machine learning, has attracted much attentions in recent years [34] due to three major attributes, i.e., unsupervised feature learning, strong generalization capability and big-data training, compared with shallow models. It is
naturally a kind of alternative of shallow models and has been widely
implemented in pattern recognition, image processing, fault detection,
classification and forecasting tasks [35]. The authors in [36] recommended a deep stochastic architecture based on Boltzmann machine for automatic feature extraction. The obtained features are very
informative and suitable for wind energy forecasting. Chang proposed a
new integrating method based on grey theory and deep belief network
for day-ahead PV power output, demonstrating that the forecast accuracy and computational efficiency are superior to the benchmarks [37].
The authors in [38] developed a new deep machine learning algorithm
to predict short-term wave energy. The forecasting results is helpful for
the real-time control and optimal management of wave energy. The
simulation shows that the prediction error has a negative impact on the
model predictive control performance, resulting in a decrease in wave
rotational inertia of the power system and thus reduces the stability
margin of the system. Therefore, renewable energy forecasting as an
effective measure is essential for mitigating related uncertainties, which
is conducive to the planning, management and operation of electrical
power and energy systems [4]. However, accurate renewable energy
forecasting remains a challenging task due to the intermittent, chaotic
and random nature of renewable energy data. Various algorithms have
been reported in the literature to provide accurate renewable energy
predictions for the next few minutes to the next few days. They are
usually divided into four categories: physical methods, statistical
models, artificial intelligence techniques and their hybrid methods [5].
Physical methods are based on numerical weather prediction (NWP)
models that simulate the atmospheric dynamics according to physical
principles and boundary conditions [6]. NWP models contain limited
area models, such as fifth-generation mesoscale model and high resolution rapid refresh, and global models, e.g., global forecast system
and integrated forecast model [7]. Many meteorological and geographical information, including temperature, pressure, jaggedness and
orography, are considered as input to NWP. Although physical methods
are efficient in forecasting atmosphere dynamics, they require large
computational resources because a lot of data is needed to calibrate that
[8]. This is even worse when physical methods encounter unexpected
errors during prediction. Therefore, physical methods are not suitable
for short-term forecasting horizons. Statistical models aim to uncover
the mathematical relationship between online time series data of renewable energy [9]. Auto regressive moving average [10], Bayesian
approach [11], Kalman filter [12], Markov Chain model [13] and gray
theory [14] were widely adopted in the literature. In [15], a new
forecasting algorithm based on Hammerstein model capable for discovering different asymmetric distribution, non-stationary profile and
chaotic dynamics of wind energy was developed. The authors in [16]
proposed a Bayesian-based adaptive robust multi-kernel regression
model for deterministic and probabilistic wind power forecasting. In
[17], a Kalman filter and time-varying regression method were proposed to realize the time-series prediction of wave energy. The case
study shows that the proposed method has the best prediction results.
However, most of the existing statistical models for renewable energy
forecast are formulated as a linear models that limit their ability to deal
with more challenging prediction problems with longer forecasting
time horizons.
With the development of soft-computing technique, artificial intelligence based forecasting models always provide a more promising
performance than physical methods and statistical approaches due to
their potential abilities for data-mining and feature-extracting [18].
Support vector machine [19], artificial neural network [20], extreme
learning machine [21] and adaptive fuzzy neuron network [22] were
frequently adopted to handle the nonlinear relationship between input
and output via error minimization. A hybrid of mixed data sampling
regression and back propagation neural network was developed to
perform real-time forecasting of carbon prices in Shenzhen, resulting in
a better performance [23]. The authors in [24] proposed a weather
classification model for day ahead photovoltaic power forecasting
based on generative adversarial networks and convolutional neural
network, and it was found that weather classification plays a decisive
role in determining the most efficient photovoltaic power forecasting
model. In [25], a new wave energy forecasting framework based on
ANN was proposed. The inputs of this framework contain historical
wave height and weather data around the wave measurement station.
The output is the current peak height of the wave energy. The validity
of the forecasting framework was verified on the measurement data
from the east coast of China. A detailed comparison of the existing
models for renewable energy forecasting was given in [26], showing
that each single model has advantages and disadvantages. Therefore,
the papers in the fourth category suggest how to combine different
forecasting methodologies to take advantage of the benefit of each individual method. For example, a hybrid forecasting system consisting of
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Energy Conversion and Management 198 (2019) 111799
energy absorption. In addition, deep convolutional neural network
[39], deep recurrent neural network [40] and stacked extreme learning
machine [41] have also been frequently reported for renewable energy
forecasting. It is generally recognized that deep learning based forecasting models exhibits attractive performance in terms of accuracy,
stability and effectiveness [42], which are beneficial to energy system
planning, scheduling and management [43]. Up to date, statistics show
that more than 100 publications have concentrated on deep learningbased forecasting models. However, to the best of our knowledge, there
is still not a single paper to review them all together. Therefore, a
comprehensive review paper targeting at deep learning based renewable energy forecasting is a pressing need to summarize the progress of
current research and provide systematic assessment of the validity and
applicability of individual studies.
Recently, renewable energy forecasting remains an active hotspot in
literature and several review papers have been published. The Ref. [44]
reviewed PV power generation forecasting techniques from the viewpoint of time-series statistical, physical, and ensemble methods. The
authors in [45] provided the readers with an extensive overview of
various techniques for photovoltaic power forecasting within a very
short-term horizon. G. C. Cristobal et al. [46] presented a survey on
wind energy ramp forecasting, which is beneficial to achieve the large
integration of wind energy. In addition, the state of the art on wind and
solar energy forecasting has been systematically reviewed from the
perspectives of cooperative and competitive ensemble methods [47].
The consequence, operational cost and benefit of solar and wind energy
forecast on electrical power and energy systems have been synthesized
[48]. In [49], the authors discuss the progress of sea wave energy operation prediction subject to energy balance, and quantitatively analyze
the interdependence of wave energy and thermal energy by studying
the input function of wind energy in detail. Nevertheless, so far, the
review of renewable energy forecasting from the perspective of deep
learning has not yet been investigated, even the related researches
flourish in recent years. Therefore, this paper aims to fill this gap.
Compared with the existing studies on similar topics, the main contribution of this paper is to review renewable energy forecasting literature from the viewpoint of deep learning based methods. Concretely,
we summarize the basic structures of deep learning as well as the associated training mechanisms and classify them into four categories,
including deep belief network, stacked auto-encoder, deep recurrent
neural network and others. We explore how deep learning-based
models improve forecasting accuracy. Existing techniques, e.g., data
preprocessing and error post-correction methods, are summarized and
dissected. Furthermore, we also discuss the current research activities,
challenges and potential future research directions.
This paper is organized as follows. Section 2 gives a general introduction and classification of deep learning based renewable energy
forecasting. The frequently-used deep architecture for deterministic and
probabilistic renewable energy forecasting are summarized in Section
3. Several techniques used for accuracy improvement are discussed in
Section 4. We also present the statistical promising performance, potential challenges and possible research directions of deep learning
based methods in Section 5. Finally, conclusions are drawn in Section 5.
u
Reconstructi
on Layer
Hidden
Layer
...
...
Input Layer
...
H. Wang, et al.
ding
Enco
y Deco
ding
u'
Fig. 1. The basic unit of an auto-encoder.
2.1. Stacked auto-encoder
A stacked auto-encoder (SAE) is a feedforward neural network
consisting of multiple layers of auto-encoders, in which the outputs of
each layer is connected to the inputs of the successive layer [50]. Each
auto-encoder (AE) is composed of an encoder and a decoder as shown in
Fig. 1, aiming to reconstruct its own inputs in an unsupervised manner.
More specifically, the encoder takes the input u ∈ R d into the hidden
layer to produce a latent map y ∈ R d . Thereafter, the decoder maps the
latent variables into a reconstruction output vector u' of the same size as
u [51]. The AE is trained to minimize the reconstruction error according
to the preset distributional assumptions over the input space. In general, the traditional squared error and cross-entropy can be used as the
minimization objective function [52].
The decoding process only adopts the latent information in hidden
layer to reproduce the inputs, indicating that the latent variables already retain much information of the input. Hence, the nonlinear
transformation defined by the encoder and decoder can be viewed as an
advanced feature extractor capable for preserving the hidden abstractions and invariant structures in input [53]. Afterwards, discarding the
decoder and hierarchically stacking the encoders create a SAE [54].
Concretely, the first layer of a SAE is trained as an independent AE,
taking the input as the training dataset. When the training process of
the first auto-encoder is completed, the hidden layer of the first AE and
the second hidden layer are treated as a new AE. The training process is
the same as the process of the first AE. Following this way, multiple
auto-encoders can be stacked hierarchically by performing the encoding rule of each layer in a bottom-up order, and a SAE is finally
formulated [55].
It has been proved in previous studies that SAE exhibits promising
and robust performance for high-level feature abstractions and representations [56], which is helpful in renewable energy forecasting.
Many forecasting models based on SAE have been developed in recent
years and we will discuss several frequently-used models in Section 3.1.
2.2. Deep belief network
Deep belief network (DBN) is first developed by Hinton [57] and has
been applied in a variety of areas. It is actually a generative graphical
model, composed of simple, unsupervised networks, i.e., restricted
Boltzmann machines (RBM), with bidirectional and symmetrical connections between different layers [58]. A restricted Boltzmann machine
acts as a stochastic neural network and consists of one layer of Boolean
visible neurons and one layer of binary-valued hidden units, as shown
in Fig. 2, where the visible and hidden layers are denoted as v and h,
with a and b representing their respective biases.
The primary objective of a RBM is to learn a probability distribution
over its input data space so that its configuration can exhibit desirable
properties [59]. The distribution is learned via a minimization of an
energy model, which is designed as a function of network parameters
based on thermodynamics. The activation probability of hidden layer
2. Basic structures of deep learning
This section presents the basic structures of deep learning, which
plays a key role in accuracy improvement for renewable energy forecasting. Generally, three main types of deep learning, including stacked
auto-encoder, deep belief network and deep recurrent neural network,
were frequently implemented in the literature. In addition, stacked
extreme learning machine, deep reinforcement learning and deep
convolutional neural network based forecasting models have also been
reported. We now elaborate their basic structures and associated
training mechanisms.
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h3
h2
h1
b2
b1
b3
hnh
h4
steps because the relationship between representation features can be
expressed more easily. The second formulation of deep RNN is to
deepen the hidden-to-output function, allowing the hidden states to be
more compact. The most benefit of this formulation attributes to its
high efficiency to summarize the history of previous inputs, making it
easier to predict the real-time output [67]. Deep hidden-to-hidden
transition is the third type of deep RNN. It adds a new data source to the
sum of the previous inputs represented by the fixed-length hidden
states. The hidden-to-hidden transition allows the hidden layers to rapidly adapt to the varying patterns of the input, while still retaining a
useful summary of the past information. The main advantage of the
deep hidden-to-hidden transition is its universal approximation property. Finally, stacking multiple recurrent hidden layers on top of each
other makes up the fourth type of deep RNN. This structure encourages
each stacked layer to operate at a different timescale. In other words,
stacked RNN can address multiple time scales in the input sequence
[68]. The outlines of the four typical deep RNNs are sketched in Fig. 4.
Various deep RNN models have been proposed for renewable energy
forecasting [69]. However, deep RNNs may increase the computation
complexity, especially when the time series data exhibits long tails
[70]. One feasible solution is to adopt recurrent and convolutional
operators for model development. Another possible solution attributes
to the use of bidirectional calculations that can capture the impacts of
both past and future states. Deep RNN can have additional stored state,
which are under direct control by the neural network. In addition, the
stored states can also be substituted by another neural network with
time delays or feedback loops. Such controlled storages are the cornerstone of long short-term memory network and gated recurrent units.
They both have unique temporal dynamic behavior and can mitigate
the exploding and vanishing gradient problems [71]. Therefore, long
short-term memory and gated recurrent network exhibit promising
performance for renewable energy forecasting [72].
bn h
b4
W
a2
a1
v1
a3
v2
a nv
a4
...
v4
v3
vnv
Fig. 2. The basic unit of an Boltzmann machine.
given the visible layer and the probability of visible layer given the
hidden layer can then be estimated iteratively to determine the network
parameters. However, the estimation process involves the determination of reconstructed-data-driving probabilities over visible and hidden
layers, which is very complicated in reality. One feasible solution is to
apply alternating Gibbs sampling [60] on any stochastic states of the
neurons until certain convergence criterion, such as k-steps, is satisfied.
In addition, contrastive divergence algorithm is generally integrated to
accelerate the sampling process with two tricks [61]: (1) Initialing the
Markov chain with a training sample; (2) Obtaining samples after only
k-steps of Gibbs sampling.
The training process of DBN is to use the unsupervised greedy algorithm to pre-train the network parameters. It has the following four
main steps [62]: (1) Adequately training the first RBM based on alternating Gibbs sampling and contrastive divergence algorithm; (2) Fixing
the network parameters and thresholds of the first RBM, and then using
the values of its hidden neurons as the input vector of the second RBM;
(3) Stacking the second RBM above the first RBM as long as the second
RBM is fully trained; (4) Stacking the other RBMs one by one according
to the procedures (2)–(3). The training procedure and binary architecture make DBN very effective for feature extractions and thus attractive in many applications, such as time series forecasting [63].
2.4. Other deep learning structures
Many other deep learning structures have been proposed for feature
extraction, such as deep convolutional neural network (DCNN), stacked
extreme learning machine and generative adversarial networks. DCNN
acts as a variation of multilayer perceptions with minimal preprocessing based on translation invariance features and shared-weights architecture [73]. It was inspired by biological information processes, in
which the connectivity arrangement between neurons recreates the
animal visual organization. Basically, DCNN consists of several alternating convolution layer and pooling layer. The convolution layer
adopts a convolution operator to map the low-level maps with local
features into several high-level maps with global features [74]. Weight
sharing technique is generally applied in convolution layer to reduce
the memory footprints and number of network parameters, simplifying
the feed forward and back propagation process. In this technique, all
neurons in the same output map share the same weight and bias with
inputs from neurons at different locations. Pooling layer is actually a
more concise representation of the input maps. It reduces the data dimensions by converting the neuron clusters at input layer into a single
neuron in the output layer. Average pooling and max pooling methods
are frequently used in this layer. Stacking the convolution layer and
pooling layer alternatively forms a DCNN structure, as shown in Fig. 5.
Stacked extreme learning machine (SELM) is a feedforward neural
network with multilayer. SELM divides a large extreme learning machine (ELM) neuron network into multiple stacked small ELMs [41].
The first two layers are actually an original ELM, in which the parameters of hidden neurons, including weights and biases, are randomly
generated. While, the parameters of the following ELM can also be
generated in a random manner or inherited from their ancestors, expect
the output weight vector that will be propagated after being cut down
to an appropriate dimension. Thus, the input information is transmitted
to the next ELM as long as the previous ELM is well-trained. Following
2.3. Deep recurrent neural network
Deep recurrent neural network originates from recurrent neural
network (RNN), which is a class of artificial neural network where
connections between nodes create a directed graph [64]. It models the
temporal dynamic behaviors exhibited in time series data via the use of
feedback connections to recall the neural states at previous time steps.
A typical structure of an RNN is shown in Fig. 3. Unlike feedforward
neural networks, RNN is capable to use the neural internal states to
process time series sequences of inputs, making them appropriate for
renewable energy forecasting [65].
There are four different ways to formulate deep RNN from conventional RNN. The first way is to learn more non-temporal structure
from the inputs by deepening the input-to-hidden function. It is found
that this formulation tends to better flatten the manifolds near which
the data concentrates and disentangle the underlying variation factors
than the original inputs [66]. The deep input-to-hidden structure also
makes it easier to learn the temporal correlation between multiple time
...
...
...
Input Layer
Hidden
Recurrent
Layer
Output Layer
Fig. 3. A typical structure of an recurrent neural network.
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yt
yt
ht-1
ht-1
yt
ht
ht-1
ht
ht
xt
xt
xt
(a) Conventional RNN
(c) Deep RNN with deep hiddento-output function
(a) Deep RNN with deep inputto-hidden function
yt
yt
zt-1
zt
(e) Deep stacked RNN
ht-1
ht
xt
ht-1
ht
(d)
xt
(d) Deep RNN with deep hiddento-hidden function
Fig. 4. The four typical formulations of deep RNN.
the input to some desired output class label [76]. Generally speaking,
the generative network generates candidates while the discriminative
network evaluates them. The most benefit of GAN is its potential to
understand and explain the underlying structure of the input dataset
even when there are no labels [77]. This benefit is very promising when
dealing with renewable energy forecasting, because the unsupervised
features in input data can be learned automatically.
Convolution
Layer
Pooling Layer
Convolution
Layer
Pooling Layer
3. Deep learning based forecasting models
Fig. 5. The DCNN structure.
In the Section 2, various deep learning models are introduced.
However, these models are actually applied to feature extraction and
cannot be directly used for renewable energy forecasting. This section
details the general structure of deterministic and probabilistic renewable energy forecasting based on deep learning.
this way, the multilayer ELM structures create a deep-learning model
capable for feature extraction in renewable energy forecasting.
Generative adversarial network (GAN) is another typical unsupervised learning method. It consists of a generative network and a
discriminative network. These two networks contest with each other in
a zero-sum game framework [75]. The generative network aims to learn
the joint probability distribution of the input data via Bayes rule, and
the discriminative model tries to learn a mapping function that maps
Renewable
energy dataset
Data
Preprocessing
Techniques
3.1. Deterministic forecasting models
In general, the deep learning based point forecasting framework of
renewable energy is shown in Fig. 6. As shown, it contains data
Low frequency
component
Deep learning
feature extractor
Regression
method
Medium
frequency
component
Deep learning
feature extractor
Regression
method
High frequency
component
Deep learning
feature extractor
Regression
method
Network structure optimization and
parameters tuning
Fig. 6. The general framework for renewable energy forecasting.
5
Renewable signal
reconstruction
Error Postprocessing
Techniques
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H. Wang, et al.
uninteresting because it hinders its interpretability. The trend component is the remaining component with better behaviors. It reveals the
trend of the original signal and is therefore predictable. Existing forecasting methods can be applied for forecasting of the trend component.
The seasonal prediction is used to correct the prediction of the trend
component to obtain the final prediction result [92]. Similarly, variational mode decomposition decomposes a time series signal into several
band-separated modes with specific sparsity properties [93]. Each
mode, i.e., subseries, has a predictable characteristics. Several studies
have focused on the introduction of this decomposition algorithm for
renewable energy forecasting [94]. In addition, other decomposition
methods, such as atomic sparse decomposition, intrinsic time-scale
decomposition and bernaola galvan algorithm, have also been adopted
for signal decomposition in current literature [95].
preprocessing techniques, feature extractor based on deep learning,
regression methods and error post-processing techniques. At first, data
preprocessing techniques are used to decompose raw renewable energy
time series data into several components with different frequencies.
Each components exhibits better outliers and behaviors than the original data. Then, a feature extractor and a regressor are developed independently for forecasting of each component. The network structures
and model parameters can be well-tuned by using the existing optimization techniques. Subsequently, the forecasting results are reconstructed by combining all of the forecasted components. Finally,
various error post-processing techniques can be further applied to
correct the reconstructed forecasting results.
3.2. Data preprocessing techniques
3.3. Feature extractor based on deep learning
Original raw renewable energy data always exhibits a variety of
irregularities, such as fluctuation and spike [78]. These irregularities
have nonlinearity and non-stationarity features, and thus deteriorates
the forecasting performance [79]. Therefore, many data preprocessing
techniques have been proposed to decompose the renewable energy
signal into several components with better behavior in terms of data
variance and outliers. With the help of these data preprocessors, the
negative impact of irregularities on forecasting accuracy can be appropriately mitigated. In literature, wavelet decomposition (WD) and
empirical mode decomposition (EMD) are two of the most widely used
methods [80]. Besides, other decomposition approaches, such as
Fourier transform, seasonal adjustment method [81] and variational
mode decomposition [82], have also been reported. WD consists of
wavelet transform and wavelet packet decomposition. Both of them are
implemented for multi-resolution analysis of time series data in both
time and frequency domain. A low-pass and a high-pass filter are respectively used to obtain the approximate and detail subseries [83]. The
difference between wavelet transform and wavelet packet decomposition is that the former decomposes the original signal into one low
frequency component and several high frequency components, while
the latter divides the original signal into several low and high frequency
components. It has been demonstrated in [84] that WD techniques are
very helpful in forecasting performance improvement because the decomposed sub-signals always exhibits better outliers and lower uncertainties.
EMD, also termed as Hilbert–Huang transform, was proposed in
1996 to decompose a signal into intrinsic mode functions (IMFs), resulting in several instantaneous frequency data. The IMFs are estimated
by the following two conditions [85]: (1) The average of the outer
envelopes is close to 0; (2) The difference between the number of zero
points and the number of extreme points of the original signal is up to 1.
EMD retains the characteristics of the varying frequency in the decomposition process because the IMFs are decomposed in time domain
and have the same length as the original signal [86]. This is the main
benefit of EMD since real-world signal generally has multiple reasons
happening in different time intervals [87]. Therefore, EMD offers a new
method for analyzing nonlinear and nonstationary data. Various studies
adopt EMD as the decomposition process and it is found that EMD also
helps in accuracy improvement for renewable energy forecasting than
the algorithms without EMD [88].
The Fourier transform is an important technique in signal processing and digital electronics. It is used to decompose an original signal
into various sine and cosine components [89]. Each component represents a specific frequency in time domain. The most benefit of
Fourier transform is that it can cancel out random noise and reveal the
trend of frequency changes [90]. However, its shortcoming is also obvious, that is, too many frequency components will undoubtedly increase the computational burden. The seasonal adjustment method is a
statistical method that divides the input signal into seasonal components and trend components [91]. The seasonal component indicates
the seasonal variation of the time series signal and is considered to be
The complexity of renewable energy forecasting lies in the significant irregularities in terms of uncertainty and volatility. According
to the previous analysis [96], the decomposition signal consists of three
parts: (1) A regular pattern that is used to describe the periodic signal
inherited from the historical samples. It is a major component and can
be accurately predicted due to its predictability. (2) Uncertainty is a
non-periodic component caused by external factors such as the natural
environment, weather and climate. This component is affected by
random factors and is therefore very difficult to predict. (3) Noise as the
component that cannot be physically explained. This term is usually
ignored and discarded because it is unpredictable. Obviously, the
forecasting of uncertainty involves the study of highly-nonlinear,
complex relationship and correlations in data, which may go beyond
the traditional shallow learning framework [97]. On the contrary, it has
been proved in previous studies that deep learning algorithms has a
universe approximation ability to extract the deep nonlinear features in
data [98], making deep learning very suitable for forecasting of renewable energy. The most important benefit of deep learning architecture is that it can learn the features in renewable energy data hierarchically [96]. The neural network architecture in different levels
learns the features in different sharing levels. Normally, the features in
higher levels are learned as a combination of the features in lower levels. Generally speaking, deep learning algorithms can be used as feature extractors in an unsupervised manner. The obtained features are
informative in renewable energy forecasting. All the algorithms introduced in Section 2, including SAE, DBN, DRNN, DCNN and GAN, can
be used in this way.
The features learned from deep learning cannot be directly used for
renewable energy forecasting. A regression process is required to map
the nonlinear features into the final forecasting results in a supervised
manner [99]. Linear regression, nonlinear regression or even neural
networks can be used in the regression process. Linear regression is to
model the relationships between dependent variable and explanatory
variables by fitting a linear equation to the observed data [100]. The
linear equation describes how the mean response varies with the explanatory variables. Nonlinear regression is a statistical form of regression analysis. In this method, the observations are formulated as a
nonlinear function whose parameters are dependent on the explanatory
variables [101]. A method of successive approximations is generally
used to fit the observation data. Many neural networks, including back
propagation, support vector machine and extreme learning machine,
can also be used at the end of the deep learning architecture to complete the regression task so that the learned features can be mapped into
the real observations [102].
3.4. Network structure optimization and parameters tuning
Another issue with regard to the forecasting framework based on
deep learning is that the network topologies are not unique. For
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the forecasting models [121]: (1) the errors resulted from the forecasting model misspecification; (2) the errors of renewable energy data
noise due to the stochastic nature of the weather system. Previous
studies have shown that ensemble techniques can improve the forecasting accuracy, cancel out the diverse errors and provide quantitative
analysis of uncertainties in renewable energy data [122]. For example,
the research in [123] developed an analog ensemble method to forecast
the day-ahead photovoltaic power by using open weather forecasts and
power measurement data. The results show that the normalized root
mean square error is improved by 13.80%–61.21% compared with
three benchmark models. Also for example, a new neural network ensemble proposal based on particle swarm optimization is developed for
photovoltaic output power forecast [124]. In addition, the trimming
aggregation method is used to eliminate the upper and lower prediction
error extremes.
Data fusion aims to integrate multiple predictions to generate more
accurate, consistent and useful information than any individual forecaster’s results [125]. It allows analysts to make inferences beyond
those that could be achieved via the use of single-source data alone.
Renewable energy forecasting may benefit from data fusion technique
to combine various information from multiple sources and the output
from different forecasting frameworks. The authors in [126] proposed a
new wind power forecasting method based on model structure selection
and data fusion technique for dimensionality reduction. According to
the actual data of wind farms in Jiangsu Province, China, the feasibility
and effectiveness of the method are verified. In addition, a hybrid
method based on Euclidean distance clustering, Markov chain and data
fusion was proposed for prediction of day-ahead photovoltaic power
[127]. The results show that the forecasting model has good prediction
accuracy and is potentially practical and feasible for short-term time
series prediction.
example, the number of the neurons in each layer as well as the number
of hidden layers can be freely-designed and may be totally different
from one designer to another. Also, the model parameters of regression
may fall into a local minima [103]. Apparently, the selection of the
neural network structure and model parameters is critical for enhancing
the forecasting accuracy. However, the selection process is very complex because it is highly associated with the input, data pattern, preprocessing, and training technique [104]. Therefore, to find the global
minimum of the regressor and to uniquely determine the network topology, trial-and-error method and many heuristic optimization algorithms have been proposed.
Trial and error is a fundamental method of problem solving that is
characterized by repeated attempts which are continued until success or
until the agent stops trying. According to this method, the researchers
can choose the network topology and model parameters based on their
sufficient experience to improve the forecasting accuracy. For instance,
the Ref. [105] used a simple trial-and-error method to find the appropriate number of neurons in an artificial neural network for precision
balance. In other cases, heuristic optimization techniques have been
implemented to determine the optimal neuron network structure and
parameters. Up to date, genetic algorithm (GA) and particle swarm
optimization (PSO) were frequently used for this purpose. GA is a
common metaheuristic inspired by the process of natural selection. It
generates high-quality solutions by taking advantages of bio-inspired
operators such as mutation, crossover and selection. GA is one of the
typical evolution algorithms in literature that are applied to optimize
weights and other features of forecasting methods [106]. PSO is another
well-established computational method that optimizes a problem by
iteratively trying to improve the solution quality of a candidate. Its
advantage contains higher learning speed and requiring less memory.
The weights and bias of a neural network can be optimized using PSO
so that the improvement of forecasting accuracy can be achieved [107].
In addition, grey wolf algorithm [108], water cycle algorithm [109],
whale optimization [110], ant lion approach [111], wind driven optimization [112] and backtracking search algorithm [113] have also been
used in different practical applications for optimization. These optimization algorithms can be used to optimize the methods of renewable
energy forecasting based on deep learning.
3.6. Probabilistic forecasting models
In practical electric power and energy system, deterministic point
forecasts might not be sufficient to characterize the inherent uncertainty of renewable energy data [128]. Therefore, probabilistic
forecasts that provide quantitative uncertainty information of renewable energy are expected to assist the planning, management and operation of the electric energy systems. The probabilistic forecasting
method focuses on assigning a probability to each prediction result
[129]. A complete probability set represents a probabilistic prediction.
Existing methods for probabilistic renewable energy prediction can be
divided into parametric and nonparametric methods, with or without
distribution shape assumptions [130]. In parametric methods, it is
generally assumed that the time series data of renewable energy follows
a prior distributions, such as Gaussian [33], beta [131] and Gamma
distributions [132]. Once the distribution is predefined, various statistical methods can be used to evaluate its parameters, such as auto-regression model, maximum likelihood, and fast Bayesian approach. In
[133], Pinson proposed a parametric auto-regression model to statistically assess the distribution parameters of historical wind power. The
superiority of the proposed method is demonstrated on the basis of 10mins ahead probabilistic forecasting at the Horns Rev wind farm in
Denmark. In [134], a new multivariate Kalman filter model was proposed for multi-step probabilistic wind power forecasting. The model
parameters was updated in real-time by a quasi-maximum likelihood
method based on expectation maximization algorithm. A stochastic
time series generated by Bayesian approach was taken to construct the
probabilistic prediction intervals on horizons of 15 min and 24–48 h
[135]. An improvement of 27–31% is demonstrated when compared to
probabilistic persistence. Nevertheless, parametric probabilistic forecasting methods tend to extend deterministic forecast into probabilistic
ones, i.e., a deterministic forecaster is required in advance. Therefore,
nonparametric approaches dominate in probabilistic forecasters.
Instead of using prior distributions, non-parametric methods are
3.5. Error post-processing technique
In recent years, although various advanced forecasting models have
been developed, they all continue to exhibit systematic errors which are
required to be carefully corrected using error post-processing methods
[114]. The primary objective of post-processing methods is to learn a
function of conditional probability distribution that relates the dependent variable of interest to predictors [115]. Up to date, machine
learning, statistical methods, ensemble technique and data fusion were
proposed to achieve this task [116]. From a viewpoint of machine
learning, error post-processing can be taken as a supervised learning
task. In [117], support vector machine was employed to find a nonlinear map from the input data to output space, minimizing the forecasting error obtained from a deterministic predictor. The Ref. [118]
used artificial neural network to correct the results of a neuro-fuzzy
forecasting model. Statistical methods consider error post-processing in
a distributional regression framework of input variables. Bayesian
model averaging and Kalman filter were two most prominent approaches. In [119], wind-speed forecast in southwest Ireland was performed over one year by using the operational HARMONIE mesoscale
weather forecast model, with Bayes model averaging for statistical postprocessing to remove local systematic bias. The hour-ahead forecast of
global horizontal irradiance was refined using Kalman filter in [120]
and a better performance was obtained.
Ensemble, also known as non-homogeneous regression, is a typical
Monte Carlo analysis technique. In this technique, multiple simulations
are carried out to account for the two common sources of uncertainty in
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estimation was proposed for wind power probability density prediction
[147]. Simulation results show that the method can construct a more
accurate prediction interval. The authors in [148] proposed a kernel
density estimation method based on logarithmic transformation to estimate the uncertainty in wind energy. Extensive comparisons were
made to show the promising performance of the proposed method.
Analog ensemble is actually a hybrid method combining numerical
weather prediction (NWP), past NWP forecast and photovoltaic power
measurement. This method first finds the past forecasts of meteorological and environmental variables that are similar to the current
forecast [149]. The previously measured power generation is then used
to develop a density function. Alessandrini et al. [150] developed an
application framework of analog ensemble to generate probabilistic
forecast of wind power. The forecasting performance is extensively
compared with three state-of-the-art methods. Cervone et al. [151]
proposed a probabilistic forecasting methodology based on artificial
neural network and analog ensemble to generate 72-hour ahead probability density function of solar power. The main benefit of analog
ensemble is its high computational efficiency because it only requires
the physical model to run once. The state-of-the-art probabilistic forecasting methods of renewable energy are partly tabulated in Table 1.
Table 1
Probabilistic forecasting methods used in the literature.
Category
Methods
Ref.
Parametric method
Auto-regression model
Maximum likelihood
Bayesian approach
Quantile regression
Bootstrapping method
Lower upper bound estimate
Gradient boosting
Kernel density estimation
Analog ensemble
[133]
[134]
[135]
[137,138]
[139,140]
[141,142]
[144,145]
[146,147,148]
[149,150,151]
Nonparametric method
developed based on distribution-free principle, and probability quantiles are estimated by a limited number of observations. As non-parametric approaches make fewer assumptions, their applicability is much
wider than parametric methods [136]. To date, various nonparametric
methods have been proposed for probabilistic forecast of renewable
energy, including quantile regression, bootstrapping, lower upper
bound estimate, gradient boosting, kernel density estimation and
analog ensemble. Quantile regression is used as an extension of linear
regression to estimate the conditional median and other quantiles of the
response variable. Koenker & Bassett proposed this method in 1978 to
minimize the absolute residual of each quantile. The authors in [137]
investigated the increasing share of photovoltaic power on prediction
intervals in net load. The interval was constructed using quantile regression that produces a probability density function. The disadvantage
of quantile regression is that it can only provide a range of given percentages [138]. Bootstrapping is commonly used as a method to evaluate the probability distribution with alternative random variables. It
allows assigning measures of accuracy in terms of bias, variance and
confidence intervals to sample estimates. Due to its simplicity, bootstrapping method is widely applied in probabilistic forecasting. A novel
nonparametric predictive density method was proposed for short term
probabilistic forecasting of solar radiation based on data-driven and
bootstrapping method [139]. It has been demonstrated that the bootstrapping based probabilistic forecasting method is computationally
efficient and has an attractive performance [140].
Khosravi [141] developed a lower-upper bound estimation method
to directly improve the quality of the prediction interval, i.e., prediction
interval coverage probability, prediction interval normalized average
width and coverage width-based criterions. It uses two neural networks
simultaneously to create probabilistic information. One neural network
is used to construct the upper bound of the prediction interval and the
other is used to construct the lower bound. In [142], the traditional
lower upper bound estimation method was reformulated as a constrained single objective problem, using PSO to optimize model parameters. Wind power time series data collected from Capital Wind Farm
was used to validate the proposed method. Gradient boosting is a
powerful machine learning technique for classification and regression
problems. It combines the output of many weak leaners to develop the
probabilistic model in a stage-wise fashion [143], allowing optimization of a loss function. In [144], a gradient boosted regression tree
model was proposed for multi-site prediction of solar power generation,
with forecast horizons ranging from 1 to 6 h. In [145], a bootstrap
based ensemble method was originally put forward to generate the
prediction intervals from multiple forecasters.
Kernel density estimation is a non-parametric method to evaluate
the probability density function of a random variable without any
distribution hypotheses. The purpose of kernel density estimation is to
smooth the contribution of each sample by applying a kernel function
with a given width on each data sample [146]. This method has been
widely applied for probabilistic forecasting of renewable energy due to
its flexibility, efficiency and smoothness. For example, a hybrid method
based on quantile regression neural network and kernel density
4. Forecasting performance analysis and discusses
To date, various deep learning based frameworks have been developed for deterministic and probabilistic forecasting of renewable
energy. We will discuss the performance improvement, challenges and
potential future research directions of these forecasting framework.
4.1. Statistical forecasting performance
Table 2 details the methods and techniques widely used in the deep
learning-based forecasting framework. DRNN, DCNN, DBN, and SAE
are often applied for feature extraction of renewable energy data to
construct the deep forecasting structures. It can be also seen that GAN,
deep multilayer perception (DMP), and SELM have only reported in a
few publications for real-time prediction of renewable energy. The main
feature of DRNN is that there are both internal feedback connections
and feedforward connections between the processing neural units.
These connections give the DRNN a memory function. Therefore, DRNN
is well suited for time-series prediction of renewable energy. The DCNN
contains pooling and convolution operations and is very suitable for
extracting typical features in an image. Therefore, DCNN is appropriate
for the case when renewable energy data has image data or can be
converted into images. The inherent basic structure of the DBN is a
restricted Boltzmann machine, and the network parameters are initialized using layer-by-layer unsupervised training method. Therefore,
the DBN can be used for renewable energy predictions when typical
features in the input data are not identifiable. SAE also uses layer-bylayer unsupervised training method, which is different from the
training method of DBN. In SAE, it is generally assumed that the output
and input are the same, and the number of neurons in the intermediate
hidden layer is less than the number of output neurons. Therefore, SAE
is suitable for the case when dimensionality reduction of input data is
required. In addition, GAN includes a generation model and a discriminant model, and the two types of models learn from each other to
produce an output similar to the input data. Therefore, GAN is usually
used in the case of a large amount of missing data in the original data of
renewable energy. The deep multi-layer perceptron is actually a traditional multi-layer neural network. The backward propagation algorithm
is generally used to train the model parameters. SELM achieves unsupervised learning of data features by simplifying the setting of network parameters. The most important feature of SELM is that its
computing is more efficient than traditional neural network. However,
its effectiveness for feature extraction of renewable energy data needs
further study because the few research on this topic is reported. The
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ELM
[8]
Support vector machine
[68,103]
Elman neural network
[153,154]
Feedforward neural network
[31,36,37,41,58,59,61,74,155,156,161,162,164,165,168]
Extreme learning machine
[154,157]
Long short term memory
[71,73,169]
Linear regression
[98]
Fuzzy regression
[21]
Logistic regression
[24,38]
Ensemble
[8,33,103,157,166]
Rough Set Theory
[31]
Gaussian mixture model
[159]
ELM
[102]
Copula theory
[167]
advantages, disadvantages and applicable scenarios of the above various types of deep learning are shown in Table 3.
In the existing literature, wavelet transform is the most commonly
used preprocessing technique. This is because WT not only decomposes
the original signal into multiple sub-signals with better behavior, but
also has higher computational efficiency. EMD and its variants, namely
variational mode decomposition, have also been reported in several
publications for renewable energy prediction based on deep learning.
This method is characterized by signal decomposition based on the
time-series features of the data itself, without the need to pre-set any
basis functions. Therefore, it is especially suitable when the renewable
energy data is very noisy. In addition, fuzzy set theory and K-means
clusters are mainly used to classify the original signals for nonlinear
curve fitting of deep learning. Grey theory preprocessing is mainly used
to filter out the noise components in the original signal. Singular
spectrum analysis is a novel signal decomposition technique that is
especially suitable for processing renewable energy data with periodic
oscillation behavior. Principal component analysis is a linear dimensionality reduction method whose goal is to map high-dimensional
data to low-dimensional space. Therefore, principal component analysis
is suitable for the case where the dimension of the input data of the
prediction framework based on deep learning is large. It is worth noting
that other pre-processing techniques mentioned in Section 3.1, such as
Fourier transform and seasonal adjustment methods, have not been
reported in deep energy-based renewable energy prediction frameworks. These data preprocessing methods deserve further study.
Deep learning based forecasting framework usually use trial-anderror method to optimize the network topology. The advantage of this
method is that it is easy to be implemented and integrated in forecasting framework. In addition, some heuristic algorithms, such as PSO,
grey wolf algorithm, differential evolution and extremal optimization,
also have been reported for topology optimization. However, heuristic
algorithms will undoubtedly increase the computational cost of the
algorithm. This is because these algorithms need to generate a large
number of particles and the final optimization results are obtained
through stochastic optimization methods. Hyper-parameter optimization and Adam optimizer are two methods commonly used to optimize
the model parameters based on deep learning. No other optimization
methods have been found in deep learning based renewable energy
prediction structures. In addition, only support vector machine, Elman
neural network, feedforward neural network, long and short time
memory networks, ELM, linear regression, fuzzy regression and logistic
regression are used in the existing literature for regression problems in
deep learning based forecasting frameworks. Among them, feedforward
neural network is the most commonly-used method. Moreover, ensemble technique, rough set theory, Gaussian mixture model, ELM and
Copula theory have been applied in the literature for error post-processing to improve the prediction accuracy and assess the uncertainty in
renewable energy data. Among them, ensemble is the most commonly
used method. It is mainly used to eliminate model misspecification and
data noise in the forecasting framework. Rough set theory is another
error post-processing technique that does not require any prior
knowledge to evaluate the dependence of errors on various factors.
Gaussian mixture model takes several single Gaussian models as input
to fit arbitrary distributed error samples. ELM is implemented to
mathematically evaluate the nonlinear relationship between the forecasting errors and the input of the forecasting framework. The Copula
theory is used to assess the dependence between errors at various time
steps. It should also be noted that other error post-processing techniques mentioned in Section 3.1, such as the Bayesian method, data fusion, lower-upper bound estimation, and kernel density estimation,
have not been reported for real-time forecasting of renewable energy. In
above, we have conducted a comprehensive analysis of the methods
used in the deep learning-based forecasting framework. We will analyze
the statistical performance of these forecasting methods, as elaborated
below.
SAE
[41,98,164,165,166]
DBN
[21,36,37,58,59,61]
DCNN
[24,31,33,38,71,73,74,155,156,161,164,169]
DRNN
[65,68,102,103,152,153,154,155,159,160,162,163,167,168]
GAN
[24]
SELM
[8,41]
Deep multilayer perceptron
[38,158]
Wavelet technique
[33,38,59,73,102]
EMD
[153,168]
Variational mode decomposition
[8,154]
Fuzzy set theory
[74]
Grey theory
[37]
k-means cluster
[58]
Singular spectrum analysis
[61]
Principal component analysis
[157]
Extremal optimization
[103]
Trial-and-error
[31,36,59,73,74,154]
Hyper-parameter optimization
[158]
PSO
[152,165]
Adam optimizer
[155]
Grey wolf optimizer
[8]
Differential Evolution
[65]
Deep learning method
Prepossessing
Table 2
Techniques used in deep learning based forecasters.
Optimization
Regression
Post-processing
H. Wang, et al.
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Table 3
The benefit, disadvantages and applicable scenarios of various deep learning algorithms.
Algorithms
Advantages
Disadvantages
Applicable Scenarios
DRNN
Capable for processing time series data;
High computation efficiency.
Capable for processing image data;
Strong capability for feature extraction.
Unsupervised feature extraction capability;
High computation efficiency.
Unsupervised feature extraction capability;
Easy to be implemented.
Capable for generating new data with the same
distribution as the input data;
Unable to effectively describe the features of
the input data;
Low computation efficiency;
The features should be better predetermined.
Disable to process multi-dimensional
renewable energy data.
Optimization of the network is difficult.
The renewable energy data has time-series data.
DCNN
DBN
SAE
GAN
DMP
Easy to be implemented.
SELM
High computation efficiency;
Unable to effectively describe the features of
the input data;
Low computation efficiency;
Unable to effectively describe the features of
the input data;
Optimization of the network is difficult.
Its ability for feature extraction has not been
fully proved;
It is similar to SAE.
Table 4
Statistical deterministic performance for forecasting of wind speed based on
DRNN.
The renewable energy data includes image or can be
converted into images.
The features of renewable energy data are not
identifiable.
Renewable energy data needs dimensionality reduction.
Renewable energy data has a lot of missing data.
There is less renewable energy data.
The computation resources is limited.
Table 6
Statistical deterministic performance for forecasting of wind speed based on
DCNN.
Data location
Indices
Ultrashort term forecast
Short-term forecast
Data location
Indices
Ultrashort term forecast
Short-term forecast
China
MAE
MAPE
RMSE
MAE
MAPE
RMSE
MAE
RMSE
MAPE
MAE
RMSE
MAPE
MAE
RMSE
MAPE
0.5746
5.4167
0.7552
0.46
4.85
0.63
0.47
0.62
4.19
1.20192
1.59568
20.56069
0.0678
0.0868
0.6913
1.141
17.1076
1.5335
0.62
8.15
0.85
0.48
0.63
4.23
0.47054
0.65827
4.84868
0.1461
0.1865
1.4901
USA
MAE
RMSE
MAPE
MAE
RMSE
MAPE
MAE
RMSE
MAE
RMSE
0.301
0.431
8.01
0.22
0.29
2.08
1.0652
1.4445
1.8456
2.5258
0.533
0.721
9.995
0.39
0.51
3.61
1.4837
2.0214
2.0055
2.6713
China
China
China
China
China
USA
USA
Table 7
Statistical deterministic performance for forecasting of wind speed based on
other deep learning algorithms.
Table 5
Statistical deterministic performance for forecasting of wind speed based on
DBN.
Data location
Indices
Ultrashort term forecast
Short-term forecast
China
RMSE
MAPE
RMSE
MAPE
RMSE
MAPE
MAE
RMSE
MAPE
MAE
RMSE
MAPE
0.2951
7.05
0.419
4.108
0.742
4.632
0.4282
0.5494
6.39
0.4926
0.64
7.04
0.9634
12.98
1.28
8.814
1.47
9.905
0.5281
0.6220
8.73
0.6197
0.9266
9.09
USA
USA
China
Australia
Algorithm
Data location
Indices
Ultrashort term
forecast
Short-term
forecast
SELM
China
SAE
Australia
MAE
RMSE
MAPE
MAE
RMSE
0.6465
0.8248
5.19
0.213
0.521
0.7167
0.9081
5.74
0.721
1.24
shallow models [65,152–154]. This is because DRNN has a memory
function that is ideal for processing time series data. In addition, both
DBN and DCNN have been frequently used for point forecasting of wind
speed. Extensive simulations also show that they have competitive
prediction performance when compared to persistence method and
statistical models no matter where the wind speed data is collected
[61,155–156]. Furthermore, it also has been demonstrated that SAE
and SELM perform better than the benchmarking forecasting models
[41,164–166]. In summary, deep learning based point forecasting
models always exhibit attractive performance. This is because deep
learning can extract the inherent nonlinear features and high-level invariant structures in sequence data.
Regarding deterministic wind power forecasting, several deep
learning algorithms based prediction models, e.g., DBN, deep feature
learning, SELM and DRNN, have been proposed in published literature.
The seasonal performance of DBN based forecasting models are tabulated in Table 8. The RMSE, MAE and MAPE of SELM, DRNN and deep
feature learning based models with different training dataset are comprehensively plotted in Figs. 7–9, respectively. Here, it should be noted
With respect to wind speed forecast in literature, the deterministic
ultrashort-term and short-term forecasting performance of DRNN, DBN,
DCNN, SELM and SAE based frameworks are statistically presented in
terms of mean absolute error (MAE), root-mean-square error (RMSE)
and mean absolute percentage error (MAPE), as shown in Tables 4–7,
respectively. It is obvious that the deterministic forecasting performance differs a lot in forecasting horizons, data locations and deep
learning algorithms. More specifically, DCNN, as the most popular deep
learning algorithm for wind speed forecasting, exhibits the most promising prediction performance when compared with the existing
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H. Wang, et al.
Table 8
Statistical deterministic performance for forecasting of wind power based on
DBN.
Data location
Spain
Indices
NRMSE
NMAE
Spring
Summer
Autumn
Winter
4.75
3.89
1.1739
2.46
1.55
3.473
3.07
2.08
4.5473
2.58
1.93
4.149
10-min-ahead
20-min-ahead
30-min-ahead
2.12
1.64
5.07
3.82
7.51
5.84
100
RMSE
80
60
40
SELM
Dataset 4
Dataset 3
Dataset 2
Dataset 1
Dataset 9
Dataset 8
Dataset 7
Dataset 6
Dataset 4
Deep feature learning
Dataset 5
Dataset 3
Dataset 2
Dataset 1
Dataset 4
Dataset 3
Dataset 1
Dataset 2
20
DRNN
Fig. 7. RMSE of deep learning based methods for forecasting of wind power.
90
80
70
MAE
60
50
40
30
20
Deep feature learning
Dataset 9
Dataset 8
Dataset 7
Dataset 6
Dataset 5
Dataset 4
Dataset 3
Dataset 2
Dataset 1
Dataset 4
Dataset 3
Dataset 2
Dataset 1
10
0
SELM
Fig. 8. MAE of deep learning based methods for forecasting of wind power.
14
12
MAPE (%)
10
8
6
4
Deep feature learning
Dataset 6
Dataset 5
Dataset 4
Dataset 3
Dataset 2
Dataset 1
Dataset 9
Dataset 8
Dataset 6
SELM
Dataset 7
Dataset 5
Dataset 4
Dataset 3
Dataset 2
Dataset 1
Dataset 4
Dataset 3
Dataset 2
Dataset 1
2
0
Spring
Summer
Autumn
Winter
−1.23%
1.91%
2.96%
1.91%
−1.52%
0.05%
2.67%
−0.99%
−0.29%
2.91%
−0.76%
−1.81%
−0.50%
−0.05%
−0.57%
−1.62%
IS
Spring
Summer
Autumn
Winter
−5.68
−3.88
−1.60
−4.27
−4.20
−3.02
−1.16
−3.18
−2.72
−1.94
−0.73
−2.06
−0.78
−0.66
−0.21
−0.55
that MAE index of DRNN is not included in Fig. 8. This is because the
existing literature on wind power forecasting based on DRNN in Table 2
does not give the MAE statistics. Therefore, we cannot present the MAE
of DRNN in Fig. 8. From Table 8, it can be seen that the RMSE in Spain
wind power dataset ranges from 1.55 to 3.89, indicating that the
forecasting performance differs a lot in different seasons. From Figs. 7
to 9, it is obvious that the deterministic performance varies greatly
according to different datasets and algorithms. However, although deep
learning based forecasting models exhibit different performance, it has
been proved that they perform better than shallow learning models and
statistical method [61,161,166]. This conclusion is consistent with that
of wind speed forecasting.
To date, probabilistic wind energy forecasting based on deep
learning has paid little attention. In [33], DCNN based ensemble forecasting method was developed. In this research, data noise and model
misspecification were evaluated by using statistical method and the
average coverage error (ACE) and interval sharpness (IS) were used as
the performance criteria to evaluate the performance of the constructed
prediction interval. Persistence method, and back-propagation/support
vector machine + quantile regression were adopted as the benchmarking algorithms. Part of the resultant probabilistic performance is
given in Table 9. Definitely, ACE and IS of the proposed method performs the best among the benchmarks, benefiting the operation and
management of electric power and energy systems.
Solar irradiance forecasting based on deep learning also attracted
little attentions in recent years. In [158], a short-term prediction
method of solar radiation based on global/local deep learning was
proposed. This method does not require local ground measurements
and only requires satellite measurements and weather forecasting information to achieve solar radiation predictions. To verify the effectiveness of the proposed method, the proposed method was tested at 25
locations in the Netherlands, as shown in Fig. 12. Linear regression,
gradient boosting tree and European Center for Medium-Range Weather
Forecast were used for performance comparison. The results show that
the proposed global deep learning and local deep learning based point
forecasting frameworks have better prediction performance in term of
RMSE.
In literature, DCNN and DBN have been implemented for deterministic forecasting of solar power. A hybrid method based on generative
adversarial network and convolutional neural network was proposed in
[24] for weather classification and accurate solar power forecasting.
GAN network was applied to augment the training dataset for each
weather types and DCNN was used for feature extraction. In [38], a
time series forecasting method based on wavelet transform, DCNN and
quantile regression was proposed for solar power forecasting. A new
forecasting model based on deep multilayer perception, support vector
machine and particle smarm optimization was developed in [152] for
deterministic forecasting of aggregated solar power. The forecasting
results contributes to the optimal economic dispatch of community
microgrid. In addition, the Ref. [155] proposed to learn the nonlinear
relationship between sky appearance and solar power output based on
DCNN. Moreover, a DRNN based model was proposed for multi-site
solar power forecasting [163]. In [169], the authors proposed a solar
power prediction model based on DCNN and long-short-term memory
120
0
ACE
Performance
RMSE
MAE
MAPE
China
Table 9
Probabilistic performance of DCNN based wind power forecasting model.
DRNN
Fig. 9. MAPE of deep learning based methods for forecasting of wind power.
11
Energy Conversion and Management 198 (2019) 111799
H. Wang, et al.
Table 10
Solar power forecasting performance of DCNN based model.
Forecasting horizon
1-hour
2-hour
4-hour
6-hour
1-hour
RMSE
MAE
MAPE
With weather data
0.10
0.05
13.42
0.012
0.06
16.49
0.16
0.08
25.17
0.23
0.13
37.83
0.14
0.12
0.06
0.07
19.57
26.39
Without weather data
Dataset in Taiwan
6
5
MAPE (%)
4
3
2
Dec.
Oct.
Nov.
Sept.
Aug.
Jul.
Jun.
May
Apr.
Feb.
Mar.
Jan.
0
DCNN
DBN
RMSE
6
4
Dec.
Nov.
Oct.
Sept.
Aug.
Jul.
Jun.
May
Apr.
Mar.
Feb.
0
Jan.
2
DCNN
DBN
48
46
44
42
RMSE
40
38
36
34
32
30
28
Linear
local DNN
GBT
Persistence
0.25
0.10
25.85
(1) Theoretical issues. The theoretical problems of deep learning are
mainly reflected in two aspects, i.e., statistics and calculation capability. For any nonlinear function, a shallow network and a deep
network can be found to represent it. Definitely, the deep learning
model has better performance than the shallow model for nonlinear
representation. However, the representation capability of deep
learning does not mean that deep learning is better for learning the
nonlinear function. Considering renewable energy forecasting, we
need to understand the complexity of predicting samples, and we
need to know how many training samples are required to learn the
deep learning network and how much computing resources are
needed for training these prediction samples. In addition, the deep
learning models are generally nonconvex functions, and it is thus
theoretical difficult for deep learning to train the deep network and
optimize its parameters.
(2) Modeling problems. Studies have shown that in the case of a large
amount of data, a complex model is more suitable to exploit the
informative features in a large amount of data. As deep learning
becomes more powerful, the features and other information extracted from large-scale predictive samples tend to be more valuable. The essence of deep learning is to learn more useful features
directly and spontaneously, and ultimately improve the prediction
accuracy. Compared to shallow learning models, deep learning has
as many as 5 layers, 6 layers, and even 10 layers of hidden neurons,
thus highlighting the importance of feature learning. Deep learning
makes learning from renewable energy time series data easier
through layer-by-layer feature learning. However, how to design a
hierarchical model with powerful feature learning is a key issue.
Moreover, establishing the most appropriate deep learning based
prediction model for a specific forecasting dataset is also one of the
problems that need to be faced.
Fig. 11. RMSE of deep learning based methods for forecasting of solar power.
Global DNN
0.12
0.06
20.27
Deep learning applications have grown rapidly because of its capability for dealing with big data and high-performance computing
power. There is already much literature on applying deep learning to
renewable energy predictions. However, deep learning based forecasting models also have the following two major challenges.
Overcoming these challenges will help to further improve the accuracy
of the deep learning prediction model.
Dataset in Belgian
Dataset in Taiwan
6-hour
4.2. Challenges
Fig. 10. MAPE of deep learning based methods for forecasting of solar power.
8
4-hour
reported in a few studies. The authors in [38] proposed a new forecasting structure based on deep multilayer neural network to predict
the changes in wave energy. The prediction results can be used in an
energy conversion controller to maximize wave energy absorption.
Model predictive control is adopted to achieve real-time latching control of the heaving point absorber. The simulation results show that the
proposed forecasting structure can accurately predict the future wave
energy, so that the efficiency of the wave energy absorber is greatly
improved.
Dataset in Belgian
1
2-hour
ECMWF
Fig. 12. RMSE of global deep multilayer perceptron (DNN) based methods for
forecasting of solar irradiance.
networks. The solar power forecasting performance of deep learning
based models was partly presented in Table 10 and Figs. 10,11. It can be
seen that the MAPE and RMSE vary a lot in terms of seasons, forecasting
horizons, solar power data locations and deep learning algorithms.
From Table 10, we can conclude that weather data is very helpful for
improvement of solar power forecasting accuracy.
The uncertainty of PV forecasting has a huge negative impact on the
daily operation and management of power systems. However, current
assessments of uncertainty in PV forecasts have not received sufficient
attention. To solve this problem, the authors in [39] proposed a probabilistic method based on DCNN for predicting future distribution of
photovoltaic power. This method can be used for uncertainty assessment of photovoltaic power. The prediction results are partly shown in
Table 11. The results show that compared with the traditional prediction models, the proposed method has the ability to improve the accuracy of uncertainty assessment.
Wave energy prediction based on deep learning has only been
4.3. Potential future research directions
From the authors’ point of view, future directions with respect to
deep learning based renewable energy forecasting models mainly
12
Energy Conversion and Management 198 (2019) 111799
H. Wang, et al.
Table 11
Probabilistic ACE and IS in Belgian based on DCNN model for photovoltaic power forecasting.
Horizon
Indices
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sept.
Oct.
Nov.
Dec.
15-minutes ahead
ACE
IS
ACE
IS
ACE
IS
ACE
IS
−1.02
−3.74
−0.83
−5.99
−1.44
−9.92
2.3
−14.45
−0.53
−9.94
−1.43
−9.33
0.43
−9.82
5.33
−21.32
−0.53
−5.43
−0.04
−6.74
−0.83
−13.63
1.45
−25.3
−1.28
−5.81
−0.18
−7.56
−0.14
−12.87
1.52
−36.75
−0.17
−4.21
0.421
−5.29
0.57
−9.01
1.16
−18.08
−0.22
−4.93
0.51
−5.8
1.31
−13.9
1.26
−39.99
0.36
−4.68
0.07
−8.17
−0.82
−16.12
1.45
−26.24
−0.78
−4.26
−0.81
−8.37
1.5
−10.95
1.45
−14.88
0.95
−5.85
2.53
−8.91
1.1
−17.06
−0.04
−23.88
0.37
−6.64
−1.44
−8.78
0.2
−14.79
−0.72
−23.98
0.43
−2.3
−0.96
−2.35
0.14
−4.36
0.98
−7.71
−0.74
−2.65
−0.87
−3.51
−0.22
−7.33
0.57
−9.46
30-minutes ahead
60-minutes ahead
120-minutes ahead
into five parts, that is, DCNN, DRNN, DBN, SAE and other deep learning
models. We introduce each type of forecasting model in detail. In addition, this paper also discusses some data preprocessing and postprocessing techniques to improve the prediction accuracy. Then, this
paper presents a large number of simulation results, which verify the
feasibility and effectiveness of the deep learning based forecasting
models. Finally, we discuss several challenges and future potential research directions for deep learning based prediction models.
The comparative discussion in this article can help renewable energy forecasting professionals decide which deep learning algorithm
can assist in improving their forecasting tools. This paper fills the existing gaps to dig out the potentials of deep learning applied for renewable energy prediction.
includes:
(1) Probability forecasting. Up to date, there have been a large number of
articles on deterministic prediction of renewable energy. However,
probabilistic forecasting models based on deep learning have not
received sufficient attention. The probabilistic forecasting model
can numerically quantify the uncertainties existing in renewable
energy time-series data. Therefore, the probabilistic prediction of
renewable energy has a significant significance for the economic
operation and daily management of the electric power and energy
system.
(2) Prediction of wave, geothermal and other renewable energies.
Currently, deep learning algorithms are mainly used for real-time
prediction and day-ahead prediction of wind energy and solar energy. The publication with respect to deep learning for predicting
wave energy is scarce. In addition, deep learning has not been applied for real-time prediction of geothermal energy. Therefore, the
prediction of other renewable energies also has a great research
value, which helps to explore the application potential of various
renewable energy resources.
(3) Feature extraction method. Another research direction in the future is
how to effectively extract features from renewable energy data. The
existing deep learning prediction model only considers a single
deep learning algorithm for feature extraction. How to integrate
multiple deep learning algorithms to effectively extract deep prediction features is a key problem that needs to be solved urgently.
(4) Combination of physical prediction model. Introducing numerical
weather prediction information into short-term prediction models is
a common strategy for improving the forecasting performance of
renewable energy. On the other hand, how to integrate the correlation between multiple ground measurements into the deep
learning based prediction model is also a valuable research direction.
(5) Unified predictive model. The deep features of renewable energy data
are different in different seasons under different climatic and topographic conditions. Therefore, the predictive model is different in
various situations. The renewable energy datasets used in the existing literature are different from each other, so it is difficult to
evaluate whether a prediction model is suitable for different data
sets. In order to evaluate the existing renewable energy forecasting
models, unified predictive methods and standards must be developed. The development of a unified renewable energy prediction
model based on seasonal, climatic and topographic information is a
major research direction in the future.
Declaration of Competing Interest
The authors declared that they have no conflicts of interest to this
work.
References
[1] Wang H, Ruan J, Wang G, et al. Deep learning based interval state estimation of AC
smart grids against sparse cyber attacks. IEEE Trans Ind Inf 2018;14(11):4766–78.
[2] Pillot Benjamin, Muselli Marc, Poggi Philippe, Dias João Batista. Historical trends
in global energy policy and renewable power system issues in Sub-Saharan Africa:
the case of solar PV. Energy Policy 2019;127:113–24.
[3] Zhao Yongning, Ye Lin, Li Zhi, Song Xuri, Lang Yansheng, Jian Su. A novel bidirectional mechanism based on time series model for wind power forecasting.
Appl Energy 2016;177:793–803.
[4] Frías-Paredes Laura, Mallor Fermín, Gastón-Romeo Martín, León Teresa. Assessing
energy forecasting inaccuracy by simultaneously considering temporal and absolute errors. Energy Convers Manage 2017;142:533–46.
[5] Hodge Bri-Mathias, Martinez-Anido Carlo Brancucci, Wang Qin, Chartan Erol,
Florita Anthony, Kiviluoma Juha. The combined value of wind and solar power
forecasting improvements and electricity storage. Appl Energy 2018;214:1–15.
[6] Zhao Xinyu, Liu Jinfu, Daren Yu, Chang Juntao. One-day-ahead probabilistic wind
speed forecast based on optimized numerical weather prediction data. Energy
Convers Manage 2018;164:560–9.
[7] Al-Yahyai Sultan, Charabi Yassine, Gastli Adel. Review of the use of Numerical
Weather Prediction (NWP) Models for wind energy assessment. Renew Sustain
Energy Rev 2010;14(9):3192–8.
[8] Hao Yan, Tian Chengshi. A novel two-stage forecasting model based on error factor
and ensemble method for multi-step wind power forecasting. Appl Energy
2019;238:368–83.
[9] Jianming Hu, Heng Jiani, Tang Jingwei, Guo Miaolin. Research and application of
a hybrid model based on Meta learning strategy for wind power deterministic and
probabilistic forecasting. Energy Convers Manage 2018;173:197–209.
[10] Aasim, Singh SN, Mohapatra Abheejeet. Repeated wavelet transform based ARIMA
model for very short-term wind speed forecasting. Renewable Energy
2019;136:758–68.
[11] Wang Yun, Wang Haibo, Srinivasan Dipti, Qinghua Hu. Robust functional regression for wind speed forecasting based on Sparse Bayesian learning. Renewable
Energy 2019;132:43–60.
[12] Yang Dazhi. On post-processing day-ahead NWP forecasts using Kalman filtering.
Sol Energy 2019;182:179–81.
[13] Wang Yun, Wang Jianzhou, Wei Xiang. A hybrid wind speed forecasting model
based on phase space reconstruction theory and Markov model: a case study of
wind farms in northwest China. Energy 2015;91:556–72.
[14] Lifeng Wu, Gao Xiaohui, Xiao Yanli, Yang Yingjie, Chen Xiangnan. Using a novel
multi-variable grey model to forecast the electricity consumption of Shandong
Province in China. Energy 2018;157:327–35.
[15] Maatallah Othman Ait, Achuthan Ajit, Janoyan Kerop, Marzocca Pier. Recursive
wind speed forecasting based on Hammerstein Auto-Regressive model. Appl
5. Conclusions
This paper provides a comprehensive review about the recent
forecasting models of renewable energy based on deep learning. Deep
learning is a multi-layer perceptron with multiple hidden layers. It
combines low-level features to form more abstract high-level features or
characterize attribute categories to discover the inherent nature of the
input data. This paper classifies deep learning based forecasting models
13
Energy Conversion and Management 198 (2019) 111799
H. Wang, et al.
[47] Ren Ye, Srikanth PN, Suganthan N. Ensemble methods for wind and solar power
forecasting—a state-of-the-art review. Renewable Sustainable Energy Rev
2015;50:82–91.
[48] Notton Gilles, Nivet Marie-Laure, Voyant Cyril, Paoli Christophe, Darras
Christophe, Motte Fabrice, et al. Intermittent and stochastic character of renewable energy sources: consequences, cost of intermittence and benefit of forecasting.
Renew Sustain Energy Rev 2018;87:96–105.
[49] Janssen Peter AEM, Bidlot Jean-Raymond. Progress in operational wave forecasting. Procedia IUTAM 2018;26:14–29.
[50] Zhang Zehan, Li Shuanghong, Xiao Yawen, Yang Yupu. Intelligent simultaneous
fault diagnosis for solid oxide fuel cell system based on deep learning. Appl Energy
2019;233–234:930–42.
[51] Lv Y, Duan Y, Kang W, Li Z, Wang FY. Traffic flow prediction with big data: a deep
learning approach. IEEE Trans Intell Transp Syst 2015;16(2):865–73.
[52] Zhao Yang, Li Jianping, Lean Yu. A deep learning ensemble approach for crude oil
price forecasting. Energy Econ 2017;66:9–16.
[53] Chen Y, Lin Z, Zhao X, Wang G, Gu Y. Deep learning-based classification of hyperspectral data. IEEE J Sel Top Appl Earth Observ Remote Sens
2014;7(6):2094–107.
[54] Qinghua Hu, Zhang Rujia, Zhou Yucan. Transfer learning for short-term wind
speed prediction with deep neural networks. Renewable Energy 2016;85:83–95.
[55] Li Shuangqi, He Hongwen, Li Jianwei. Big data driven lithium-ion battery modeling method based on SDAE-ELM algorithm and data pre-processing technology.
Appl Energy 2019;242:1259–73.
[56] Huang Tian-en, Guo Qinglai, Sun Hongbin, Tan Chin-Woo, Tianyu Hu. A deep
spatial-temporal data-driven approach considering microclimates for power
system security assessment. Appl Energy 2019;237:36–48.
[57] Geoffrey SO, Hinton E, Teh Y-W. A fast learning algorithm for deep belief nets.
Neural Comput 2006;18(7):1527–54.
[58] Wang Kejun, Qi Xiaoxia, Liu Hongda, Song Jiakang. Deep belief network based kmeans cluster approach for short-term wind power forecasting. Energy
2018;165(Part A):840–52.
[59] Wang HZ, Wang GB, Li GQ, Peng JC, Liu YT. Deep belief network based deterministic and probabilistic wind speed forecasting approach. Appl Energy
2016;182:80–93.
[60] Guoyin Fu. Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system. Energy 2018;148:269–82.
[61] Zhang Yachao, Le Jian, Liao Xiaobing, Zheng Feng, Li Yinghai. A novel combination forecasting model for wind power integrating least square support vector
machine, deep belief network, singular spectrum analysis and locality-sensitive
hashing. Energy 2019;168:558–72.
[62] Wang Huaizhi, Ruan Jiaqi, Ma Zhengwei, Zhou Bin, Fu Xueqian, Cao Guangzhong.
Deep learning aided interval state prediction for improving cyber security in energy internet. Energy 2019;174:1292–304. https://doi.org/10.1016/j.energy.
2019.03.009.
[63] Dedinec Aleksandra, Filiposka Sonja, Dedinec Aleksandar, Kocarev Ljupco. Deep
belief network based electricity load forecasting: an analysis of Macedonian case.
Energy 2016;115(Part 3):1688–700.
[64] Chuanjin Yu, Li Yongle, Bao Yulong, Tang Haojun, Zhai Guanghao. A novel framework for wind speed prediction based on recurrent neural networks and support
vector machine. Energy Convers Manage 2018;178:137–45.
[65] Ya-Lan Hu, Chen Liang. A nonlinear hybrid wind speed forecasting model using
LSTM network, hysteretic ELM and Differential Evolution algorithm. Energy
Convers Manage 2018;173:123–42.
[66] Goodfellow IJ, Le Q, Saxe A, Ng A. Measuring invariances in deep networks. Adv
Neural Inf Process Syst 2009:646–54.
[67] Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio, How to
Construct Deep Recurrent Neural Networks, arXiv: 1312. 6026v5 [cs.NE], Apr. 24;
2014.
[68] Qin Yong, Li Kun, Liang Zhanhao, Lee Brendan, Zhang Fuyong, Yongcheng Gu,
et al. Hybrid forecasting model based on long short term memory network and
deep learning neural network for wind signal. Appl Energy 2019;236:262–72.
[69] Yang Fangfang, Li Weihua, Li Chuan, Miao Qiang. State-of-charge estimation of
lithium-ion batteries based on gated recurrent neural network. Energy
2019;175:66–75.
[70] Fan Cheng, Wang Jiayuan, Gang Wenjie, Li Shenghan. Assessment of deep recurrent neural network-based strategies for short-term building energy predictions. Appl Energy 2019;236:700–10.
[71] Chen Yong, Zhang Shuai, Zhang Wenyu, Peng Juanjuan, Cai Yishuai. Multifactor
spatio-temporal correlation model based on a combination of convolutional neural
network and long short-term memory neural network for wind speed forecasting.
Energy Convers Manage 2019;185:783–99.
[72] Yu Z, Niu Z, Tang W, Wu Q. Deep learning for daily peak load forecasting–a novel
gated recurrent neural network combining dynamic time warping. IEEE Access
2019;7:17184–94.
[73] Liu Hui, Mi Xiwei, Li Yanfei. Smart deep learning based wind speed prediction
model using wavelet packet decomposition, convolutional neural network and
convolutional long short term memory network. Energy Convers Manage
2018;166:120–31.
[74] Sadaei Hossein Javedani, de Lima e Silva Petrônio Cândido, Guimarães Frederico
Gadelha, Lee Muhammad Hisyam. Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series. Energy
2019;175:365–77.
[75] Chen Zhenghua, Jiang Chaoyang. Building occupancy modeling using generative
adversarial network. Energy Build 2018;174:372–9.
[76] Tian Chenlu, Li Chengdong, Zhang Guiqing, Lv Yisheng. Data driven parallel
Energy 2015;145:191–7.
[16] Wang Yun, Qinghua Hu, Meng Deyu, Zhu Pengfei. Deterministic and probabilistic
wind power forecasting using a variational Bayesian-based adaptive robust multikernel regression model. Appl Energy 2017;208:1097–112.
[17] Reikard Gordon. Forecasting ocean wave energy: tests of time-series models.
Ocean Eng 2009;36(5):348–56.
[18] Daut Mohammad Azhar Mat, Hassan Mohammad Yusri, Abdullah Hayati, Rahman
Hasimah Abdul, Abdullah Md Pauzi, Hussin Faridah. Building electrical energy
consumption forecasting analysis using conventional and artificial intelligence
methods: a review. Renew Sustain Energy Rev 2017;70:1108–18.
[19] Deo Ravinesh C, Wen Xiaohu, Qi Feng. A wavelet-coupled support vector machine
model for forecasting global incident solar radiation using limited meteorological
dataset. Appl Energy 2016;168:568–93.
[20] Wang Jianzhou, Zhang Na, Haiyan Lu. A novel system based on neural networks
with linear combination framework for wind speed forecasting. Energy Convers
Manage 2019;181:425–42.
[21] Ali Mumtaz, Prasad Ramendra. Significant wave height forecasting via an extreme
learning machine model integrated with improved complete ensemble empirical
mode decomposition. Renew Sustain Energy Rev 2019;104:281–95.
[22] Amir Sharifian M, Ghadi Jabbari, Ghavidel Sahand, Li Li, Zhang Jiangfeng. A new
method based on Type-2 fuzzy neural network for accurate wind power forecasting
under uncertain data. Renewable Energy 2018;120:220–30.
[23] Han Meng, Ding Lili, Zhao Xin, Kang Wanglin. Forecasting carbon prices in the
Shenzhen market, China: the role of mixed-frequency factors. Energy
2019;171:69–76.
[24] Wang Fei, Zhang Zhanyao, Liu Chun, Yili Yu, Pang Songling, Duić Neven, Shafiekhah Miadreza, Catalão João PS. Generative adversarial networks and convolutional neural networks based weather classification model for day ahead shortterm photovoltaic power forecasting. Energy Convers Manage 2019;181:443–62.
[25] Jihee Oh, Suh Kyung-Duck. Real-time forecasting of wave heights using EOF –
wavelet – neural network hybrid model. Ocean Eng 2018;150:48–59.
[26] Tascikaraoglu A, Uzunoglu M. A review of combined approaches for prediction of
short-term wind speed and power. Renew Sustain Energy Rev 2014;34(6):243–54.
[27] Jiang Ping, Yang Hufang, Heng Jiani. A hybrid forecasting system based on fuzzy
time series and multi-objective optimization for wind speed forecasting. Appl
Energy 2019;235:786–801.
[28] Li Yanfei, Shi Huipeng, Han Fengze, Duan Zhu, Liu Hui. Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting
strategy. Renewable Energy 2019;135:540–53.
[29] Wang Jian-Zhou, Wang Yun, Jiang Ping. The study and application of a novel
hybrid forecasting model – a case study of wind speed forecasting in China. Appl
Energy 2015;143:472–88.
[30] Khodayar M, Wang J, Manthouri M. Interval deep generative neural network for
wind speed forecasting. IEEE Trans Smart Grid 2019;99. 1 1.
[31] Khodayar M, Wang J. Spatio-temporal graph deep neural network for short-term
wind speed forecasting. IEEE Trans Sustainable Energy 2019;10(2):670–81.
[32] de Freitas Viscondi Gabriel, Alves-Souza Solange N. A Systematic Literature
Review on big data for solar photovoltaic electricity generation forecasting.
Sustainable Energy Technol Assess 2019;31:54–63.
[33] Wang HZ, Li GQ, Wang GB, Peng JC, Jiang H, Liu YT. Deep learning based ensemble approach for probabilistic wind power forecasting. Appl Energy Feb.
2017;188:56–70.
[34] Ahmad Tanveer, Chen Huanxin. Deep learning for multi-scale smart energy forecasting. Energy 2019;175:98–112.
[35] Helbing Georg, Ritter Matthias. Deep learning for fault detection in wind turbines.
Renew Sustain Energy Rev 2018;98:189–98.
[36] Zhang C, Chen CLP, Gan M, Chen L. Predictive deep Boltzmann machine for
multiperiod wind speed forecasting. IEEE Trans Sustainable Energy
2015;6(4):1416–25.
[37] Chang GW, Lu HJ. Integrating grey data preprocessor and deep belief network for
day-ahead PV power output forecast. IEEE Trans Sustainable Energy 2019;99. 1 1.
[38] Li Liang, Yuan Zhiming, Gao Yan. Maximization of energy absorption for a wave
energy converter using the deep machine learning. Energy 2018;165(Part
A):340–9.
[39] Wang Huaizhi, Yi Haiyan, Peng Jianchun, Wang Guibin, Liu Yitao, Jiang Hui, et al.
Deterministic and probabilistic forecasting of photovoltaic power based on deep
convolutional neural network. Energy Convers Manage 2017;153:409–22.
[40] Rahman Aowabin, Srikumar Vivek, Smith Amanda D. Predicting electricity consumption for commercial and residential buildings using deep recurrent neural
networks. Appl Energy 2018;212:372–85.
[41] Luo X, et al. Short-term wind speed forecasting via stacked extreme learning
machine with generalized correntropy. IEEE Trans Ind Inf Nov.
2018;14(11):4963–71.
[42] Bedi Jatin, Toshniwal Durga. Deep learning framework to forecast electricity demand. Appl Energy 2019;238:1312–26.
[43] Hua Haochen, Qin Yuchao, Hao Chuantong, Cao Junwei. Optimal energy management strategies for energy Internet via deep reinforcement learning approach.
Appl Energy 2019;239:598–609.
[44] Sobri Sobrina, Koohi-Kamali Sam, Rahim Nasrudin Abd. Solar photovoltaic generation forecasting methods: a review. Energy Convers Manage 2018;156:459–97.
[45] Barbieri Florian, Rajakaruna Sumedha, Ghosh Arindam. Very short-term photovoltaic power forecasting with cloud modeling: a review. Renew Sustain Energy
Rev 2017;75:242–63.
[46] Gallego-Castillo Cristobal, Cuerva-Tejero Alvaro, Lopez-Garcia Oscar. A review on
the recent history of wind power ramp forecasting. Renew Sustain Energy Rev
2015;52:1148–57.
14
Energy Conversion and Management 198 (2019) 111799
H. Wang, et al.
[77]
[78]
[79]
[80]
[81]
[82]
[83]
[84]
[85]
[86]
[87]
[88]
[89]
[90]
[91]
[92]
[93]
[94]
[95]
[96]
[97]
[98]
[99]
[100]
[101]
[102]
[103]
[104]
[105] Liu J, Fang W, Zhang X, Yang C. An improved photovoltaic power forecasting
model with the assistance of aerosol index data. IEEE Trans Sustainable Energy
2015;6(2):434–42.
[106] VanDeventer William, Jamei Elmira, Thirunavukkarasu Gokul Sidarth,
Seyedmahmoudian Mehdi, Soon Tey Kok, Horan Ben, Mekhilef Saad, Stojcevski
Alex. Short-term PV power forecasting using hybrid GASVM technique. Renewable
Energy 2019;140:367–79.
[107] Alnaqi Abdulwahab A, Moayedi Hossein, Shahsavar Amin, Nguyen Truong Khang.
Prediction of energetic performance of a building integrated photovoltaic/thermal
system thorough artificial neural network and hybrid particle swarm optimization
models. Energy Convers Manage 2019;183:137–48.
[108] Song Jingjing, Wang Jianzhou, Haiyan Lu. A novel combined model based on
advanced optimization algorithm for short-term wind speed forecasting. Appl
Energy 2018;215:643–58.
[109] Yang Zhongshan, Wang Jian. A combination forecasting approach applied in
multistep wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm. Appl Energy 2018;230:1108–25.
[110] Sun Wei, Zhang Chongchong. Analysis and forecasting of the carbon price using
multi—resolution singular value decomposition and extreme learning machine
optimized by adaptive whale optimization algorithm. Appl Energy
2018;231:1354–71.
[111] Pei Du, Wang Jianzhou, Guo Zhenhai, Yang Wendong. Research and application of
a novel hybrid forecasting system based on multi-objective optimization for wind
speed forecasting. Energy Convers Manage 2017;150:90–107.
[112] Yang Zhongshan, Wang Jian. A hybrid forecasting approach applied in wind speed
forecasting based on a data processing strategy and an optimized artificial intelligence algorithm. Energy 2018;160:87–100.
[113] Zhang Chu, Zhou Jianzhong, Li Chaoshun, Wenlong Fu, Peng Tian. A compound
structure of ELM based on feature selection and parameter optimization using
hybrid backtracking search algorithm for wind speed forecasting. Energy Convers
Manage 2017;143:360–76.
[114] Okumus Inci, Dinler Ali. Current status of wind energy forecasting and a hybrid
method for hourly predictions. Energy Convers Manage 2016;123:362–71.
[115] Abuella Mohamed, Chowdhury Badrul. Forecasting of solar power ramp events: a
post-processing approach. Renewable Energy 2019;133:1380–92.
[116] Li Zhi, Ye Lin, Zhao Yongning, et al. Short-term wind power prediction based on
extreme learning machine with error correction. Protect Control Modern Power
Syst 2016;1(1):9–16.
[117] Haque AU, Nehrir MH, Mandal P. A hybrid intelligent model for deterministic and
quantile regression approach for probabilistic wind power forecasting. IEEE Trans
Power Syst 2014;29(4):1663–72.
[118] Katsigiannis YA, Tsikalakis AG, Georgilakis PS, Hatziargyriou ND. Improved wind
power forecasting using a combined neuro-fuzzy and artificial neural network
model. Advances in artificial intelligence. Berlin Heidelberg: Springer; 2006. p.
105–15.
[119] Peters Martin B, O’Brien Enda, McKinstry Alastair, Ralph Adam. Wind forecasting
using HARMONIE with bayes model averaging for fine-tuning. Energy Procedia
2013;40:95–101.
[120] Diagne Maimouna, David Mathieu, Boland John, Schmutz Nicolas, Lauret
Philippe. Post-processing of solar irradiance forecasts from WRF model at Reunion
Island. Sol Energy 2014;105:99–108.
[121] Liu Hui, Duan Zhu, Han Feng-ze, Li Yan-fei. Big multi-step wind speed forecasting
model based on secondary decomposition, ensemble method and error correction
algorithm. Energy Convers Manage 2018;156:525–41.
[122] Kim Deockho, Hur Jin. Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method. Energy 2018;157:211–26.
[123] Zhang X, Li Y, Lu S, Hamann HF, Hodge B, Lehman B. A solar time based analog
ensemble method for regional solar power forecasting. IEEE Trans Sustainable
Energy 2019;10(1):268–79.
[124] Raza MQ, Nadarajah M, Li J, Lee KY, Gooi HB. An ensemble framework for dayahead forecast of PV output in smart grids. IEEE Trans Ind Inf 2019;99. 1–1.
[125] Vaccaro A, Mercogliano P, Schiano P, Villacci D. An adaptive framework based on
multi-model data fusion for one-day-ahead wind power forecasting. Electr Power
Syst Res 2011;81(3):775–82.
[126] Shao Haijian, Deng Xing. Short-term wind power forecasting using model structure selection and data fusion techniques. Int J Electr Power Energy Syst
2016;83:79–86.
[127] Yang Xiuyuan, Minglu Xu, Shouchen Xu, Han Xiaojuan. Day-ahead forecasting of
photovoltaic output power with similar cloud space fusion based on incomplete
historical data mining. Appl Energy 2017;206:683–96.
[128] Sun Mucun, Feng Cong, Chartan Erol Kevin, Hodge Bri-Mathias, Zhang Jie. A twostep short-term probabilistic wind forecasting methodology based on predictive
distribution optimization. Appl Energy 2019;238:1497–505.
[129] Jiang Yan, Huang Guoqing, Yang Qingshan, Yan Zhitao, Zhang Chaofan. A novel
probabilistic wind speed prediction approach using real time refined variational
model decomposition and conditional kernel density estimation. Energy Convers
Manage 2019;185:758–73.
[130] Lefèvre S, Sun C, Bajcsy R, Laugier C. Comparison of parametric and non-parametric approaches for vehicle speed prediction. American Control Conference
(ACC). IEEE; 2014. p. 3494–13439.
[131] Bludszuweit H, Domínguez-Navarro JA, Llombart A. Statistical analysis of wind
power forecast error. IEEE Trans Power Syst 2008;23(3):983–91.
[132] Bracale A, Caramia P, Carpinelli G, Di Fazio AR, Ferruzzi G. A bayesian method for
short-term probabilistic forecasting of photovoltaic generation in smart grid operation and control. Energies 2013;6(2):733–47.
[133] Pinson P. Very-short-term probabilistic forecasting of wind power with
prediction of building energy consumption using generative adversarial nets.
Energy Build 2019;186:230–43.
Chen Y, Wang Y, Kirschen D, Zhang B. Model-free renewable scenario generation
using generative adversarial networks. IEEE Trans Power Syst May
2018;33(3):3265–75.
Wang Jianzhou, Yang Wendong, Pei Du, Niu Tong. A novel hybrid forecasting
system of wind speed based on a newly developed multi-objective sine cosine algorithm. Energy Convers Manage 2018;163:134–50.
Azimi R, Ghofrani M, Ghayekhloo M. A hybrid wind power forecasting model
based on data mining and wavelets analysis. Energy Convers Manage
2016;127:208–25.
Zongxi Qu, Zhang Kequan, Mao Wenqian, Wang Jian, Liu Cheng, Zhang Wenyu.
Research and application of ensemble forecasting based on a novel multi-objective
optimization algorithm for wind-speed forecasting. Energy Convers Manage
2017;154:440–54.
Wenyu Zhang, Jujie Wang, Jianzhou Wang, Zengbao Zhao, Meng Tian. Short-term
wind speed forecasting based on a hybrid model. Appl Soft Comput
2013;13(7):3225–33.
Peng Xiangang, Zheng Weiqin, Zhang Dan, Liu Yi, Di Lu, Lin Lixiang. A novel
probabilistic wind speed forecasting based on combination of the adaptive ensemble of on-line sequential ORELM (Outlier Robust Extreme Learning Machine)
and TVMCF (time-varying mixture copula function). Energy Convers Manage
2017;138:587–602.
Dhiman Harsh S, Deb Dipankar, Guerrero Josep M. Hybrid machine intelligent
SVR variants for wind forecasting and ramp events. Renewable Sustainable Energy
Rev 2019;108:369–79.
Ziel Florian, Steinert Rick. Probabilistic mid- and long-term electricity price
forecasting. Renew Sustain Energy Rev 2018;94:251–66.
Li Chuan, Tao Ying, Ao Wengang, Yang Shuai, Bai Yun. Improving forecasting
accuracy of daily enterprise electricity consumption using a random forest based
on ensemble empirical mode decomposition. Energy 2018;165(Part B):1220–7.
Santhosh Madasthu, Chintham Venkaiah DM, Kumar Vinod. Ensemble empirical
mode decomposition based adaptive wavelet neural network method for wind
speed prediction. Energy Convers Manage 2018;168:482–93.
Zhu Bangzhu, Han Dong, Wang Ping, Zhanchi Wu, Zhang Tao, Wei Yi-Ming.
Forecasting carbon price using empirical mode decomposition and evolutionary
least squares support vector regression. Appl Energy 2017;191:521–30.
Mi Xiwei, Liu Hui, Li Yanfei. Wind speed prediction model using singular spectrum
analysis, empirical mode decomposition and convolutional support vector machine. Energy Convers Manage 2019;180:196–205.
Yang Zong-Chang. Modeling and forecasting monthly movement of annual average
solar insolation based on the least-squares Fourier-model. Energy Convers Manage
2014;81:201–10.
Reikard Gordon, Hansen Clifford. Forecasting solar irradiance at short horizons:
frequency and time domain models. Renewable Energy 2019;135:1270–90.
Ribeiro Mauro, Grolinger Katarina, ElYamany Hany F, Higashino Wilson A,
Capretz Miriam AM. Transfer learning with seasonal and trend adjustment for
cross-building energy forecasting. Energy and Buildings 2018;165:352–63.
Wang Jujie, Zhang Wenyu, Wang Jianzhou, Han Tingting, Kong Lingbin. A novel
hybrid approach for wind speed prediction. Inf Sci 2014;273:304–18.
Majumder Irani, Dash PK, Bisoi Ranjeeta. Variational mode decomposition based
low rank robust kernel extreme learning machine for solar irradiation forecasting.
Energy Convers Manage 2018;171:787–806.
Naik Jyotirmayee, Dash Pradipta Kishore, Dhar Snehamoy. A multi-objective wind
speed and wind power prediction interval forecasting using variational modes
decomposition based multi-kernel robust ridge regression. Renewable Energy
2019;136:701–31.
Qian Zheng, Pei Yan, Zareipour Hamidreza, Chen Niya. A review and discussion of
decomposition-based hybrid models for wind energy forecasting applications. Appl
Energy 2019;235:939–53.
Shi H, Xu M, Li R. Deep learning for household load forecasting—a novel pooling
deep RNN. IEEE Trans Smart Grid 2018;9(5):5271–80.
Yang H, Dillon TS, Chen YP. Optimized structure of the traffic flow forecasting
model with a deep learning approach. IEEE Trans Neural Networks Learn Syst
2017;28(10):2371–81.
Khodayar M, Kaynak O, Khodayar ME. Rough deep neural architecture for shortterm wind speed forecasting. IEEE Trans Ind Inf 2017;13(6):2770–9.
Zheng Dehua, Eseye Abinet Tesfaye, Zhang Jianhua, Li Han. Short-term wind
power forecasting using a double-stage hierarchical ANFIS approach for energy
management in microgrids. Protect Control Modern Power Syst 2017;2(2):136–45.
Fang Tingting, Lahdelma Risto. Evaluation of a multiple linear regression model
and SARIMA model in forecasting heat demand for district heating system. Appl
Energy 2016;179:544–52.
Tian Meng, Yuehong Su. Multiple nonlinear regression model for predicting the
optical performances of dielectric crossed compound parabolic concentrator
(dCCPC). Sol Energy 2018;159:212–25.
Li Yanfei, Haiping Wu, Liu Hui. Multi-step wind speed forecasting using EWT
decomposition, LSTM principal computing, RELM subordinate computing and
IEWT reconstruction. Energy Convers Manage 2018;167:203–19.
Chen Jie, Zeng Guo-Qiang, Zhou Wuneng, Wei Du, Kang-Di Lu. Wind speed
forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization. Energy Convers Manage 2018;165:681–95.
Das Utpal Kumar, Tey Kok Soon, Seyedmahmoudian Mehdi, Mekhilef Saad, Idris
Moh Yamani Idna, Van Deventer Willem, Horan Bend, Stojcevski Alex. Forecasting
of photovoltaic power generation and model optimization: a review. Renewable
Sustainable Energy Rev 2018;81(Part 1):912–28.
15
Energy Conversion and Management 198 (2019) 111799
H. Wang, et al.
[134]
[135]
[136]
[137]
[138]
[139]
[140]
[141]
[142]
[143]
[144]
[145]
[146]
[147]
[148]
[149]
[150]
[151]
[152] Wen Lulu, Zhou Kaile, Yang Shanlin, Xinhui Lu. Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting.
Energy 2019;171:1053–65.
[153] Liu Hui, Mi Xi-wei, Li Yan-fei. Wind speed forecasting method based on deep
learning strategy using empirical wavelet transform, long short term memory
neural network and Elman neural network. Energy Convers Manage
2018;156:498–514.
[154] Liu Hui, Mi Xiwei, Li Yanfei. Smart multi-step deep learning model for wind speed
forecasting based on variational mode decomposition, singular spectrum analysis,
LSTM network and ELM. Energy Convers Manage 2018;159:54–64.
[155] Zhang Jinsong, Verschae Rodrigo, Nobuhara Shohei, Lalonde Jean-François. Deep
photovoltaic nowcasting. Sol Energy 2018;176:267–76.
[156] Kaba Kazım, Sarıgül Mehmet, Avcı Mutlu, Mustafa Kandırmaz H. Estimation of
daily global solar radiation using deep learning model. Energy 2018;162:126–35.
[157] Feng Cong, Cui Mingjian, Hodge Bri-Mathias, Zhang Jie. A data-driven multimodel methodology with deep feature selection for short-term wind forecasting.
Appl Energy 2017;190:1245–57.
[158] Lago Jesus, De Brabandere Karel, De Ridder Fjo, De Schutter Bart. Short-term
forecasting of solar irradiance without local telemetry: a generalized model using
satellite data. Sol Energy 2018;173:566–77.
[159] Zhang Jinhua, Yan Jie, Infield David, Liu Yongqian, Lien Fue-sang. Short-term
forecasting and uncertainty analysis of wind turbine power based on long shortterm memory network and Gaussian mixture model. Appl Energy
2019;241:229–44.
[160] Srivastava Shikhar, Lessmann Stefan. A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data. Sol
Energy 2018;162:232–47.
[161] Ruiguo Yu, Liu Zhiqiang, Li Xuewei, Wenhuan Lu, Ma Degang, Mei Yu, et al. Scene
learning: deep convolutional networks for wind power prediction by embedding
turbines into grid space. Appl Energy 2019;238:249–57.
[162] Muhammad A, Lee JM, Hong SW, Lee SJ, Lee EH. Deep learning application in
power system with a case study on solar irradiation forecasting. In: 2019
International Conference on Artificial Intelligence in Information and
Communication (ICAIIC), Okinawa, Japan, 2019, pp. 275–9.
[163] Lee J, Lee I, Kim S. Multi-site photovoltaic power generation forecasts based on
deep-learning algorithm. In: 2017 International Conference on Information and
Communication Technology Convergence (ICTC), Jeju, 2017, pp. 1118–20.
[164] Khodayar M, Mohammadi S, Khodayar ME, Wang J, Liu G. Convolutional graph
autoencoder: a generative deep neural network for probabilistic spatio-temporal
solar irradiance forecasting. IEEE Trans Sustainable Energy 2019.
[165] Jiao R, Huang X, Ma X, Han L, Tian W. A model combining stacked auto encoder
and back propagation algorithm for short-term wind power forecasting. IEEE
Access 2018;6:17851–8.
[166] Yan J, Zhang H, Liu Y, Han S, Li L, Lu Z. Forecasting the high penetration of wind
power on multiple scales using multi-to-multi mapping. IEEE Trans Power Syst
2018;33(3):3276–84.
[167] Toubeau J, Bottieau J, Vallée F, De Grève Z. Deep learning-based multivariate
probabilistic forecasting for short-term scheduling in power markets. IEEE Trans
Power Syst 2019;34(2):1203–15.
[168] Hwangbo Soonho, Nam KiJeon, Heo SungKu, Yoo ChangKyoo. Hydrogen-based
self-sustaining integrated renewable electricity network (HySIREN) using a
supply-demand forecasting model and deep-learning algorithms. Energy Convers
Manage 2019;185:353–67.
[169] Lee W, Kim K, Park J, Kim J, Kim Y. Forecasting solar power using long-short term
memory and convolutional neural networks. IEEE Access 2018;6:73068–80.
generalized logit–normal distributions. J R Stat Soc: Ser C (Appl Stat)
2012;61(4):555–76.
Poncela Marta, Poncela Pilar, Perán José Ramón. Automatic tuning of Kalman
filters by maximum likelihood methods for wind energy forecasting. Appl Energy
2013;108:349–62.
Bracale A, Carpinelli G, Falco PD, A Bayesian-based approach for the short-term
forecasting of electrical loads in smart grids. Part II: numerical applications. In:
International Symp. Power Electron. Electr. Drives, Autom. Motion, 2016, pp.
129–136.
van der Meer DW, Widén J, Munkhammar J. Review on probabilistic forecasting
of photovoltaic power production and electricity consumption. Renewable
Sustainable Energy Rev 2018;81(Part 1):1484–512.
van der Meer DW, Munkhammar J, Widén J. Probabilistic forecasting of solar
power, electricity consumption and net load: investigating the effect of seasons,
aggregation and penetration on prediction intervals. Sol Energy
2018;171:397–413.
Zhang Wenjie, Quan Hao, Srinivasan Dipti. Parallel and reliable probabilistic load
forecasting via quantile regression forest and quantile determination. Energy
2018;160:810–9.
Grantham Adrian, Gel Yulia R, Boland John. Nonparametric short-term probabilistic forecasting for solar radiation. Sol Energy 2016;133:465–75.
Nowotarski Jakub, Weron Rafał. Recent advances in electricity price forecasting: a
review of probabilistic forecasting. Renewable Sustainable Energy Rev
2018;81(Part 1):1548–68.
Khosravi A, Nahavandi S, Creighton D, Atiya AF. Lower upper bound estimation
method for construction of neural network-based prediction intervals. IEEE Trans
Neural Netw 2011;22(3):337–46.
Quan H, Srinivasan D, Khosravi A. Short-term load and wind power forecasting
using neural network-based prediction intervals. IEEE Trans Neural Networks
Learn Syst Feb. 2014;25(2):303–15.
Verbois Hadrien, Rusydi Andrivo, Thiery Alexandre. Probabilistic forecasting of
day-ahead solar irradiance using quantile gradient boosting. Sol Energy
2018;173:313–27.
Persson Caroline, Bacher Peder, Shiga Takahiro, Madsen Henrik. Multi-site solar
power forecasting using gradient boosted regression trees. Sol Energy
2017;150:423–36.
Zhang J, Wang Y, Sun M, Zhang N, Kang C. Constructing probabilistic load forecast from multiple point forecasts: a bootstrap based approach. In: 2018 IEEE
Innovative Smart Grid Technologies – Asia (ISGT Asia), Singapore, 2018, pp.
184–9.
Han Qinkai, Meng Fanman, Tao Hu, Chu Fulei. Non-parametric hybrid models for
wind speed forecasting. Energy Convers Manage 2017;148:554–68.
He Yaoyao, Li Haiyan. Probability density forecasting of wind power using
quantile regression neural network and kernel density estimation. Energy Convers
Manage 2018;164:374–84.
Zhang Yao, Wang Jianxue, Luo Xu. Probabilistic wind power forecasting based on
logarithmic transformation and boundary kernel. Energy Convers Manage
2015;96:440–51.
Alessandrini S, Delle Monache L, Sperati S, Cervone G. An analog ensemble for
short-term probabilistic solar power forecast. Appl Energy 2015;157:95–110.
Alessandrini S, Delle Monache L, Sperati S, Nissen JN. A novel application of an
analog ensemble for short-term wind power forecasting. Renewable Energy
2015;76:768–81.
Cervone Guido, Clemente-Harding Laura, Alessandrini Stefano, Monache Luca
Delle. Short-term photovoltaic power forecasting using artificial neural networks
and an analog ensemble. Renewable Energy 2017;108:274–86.
16