A Weather-Based Hybrid Method for 1-Day
Ahead Hourly Forecasting of PV Power Output
Abstract
To improve real-time control performance and reduce possible negative impacts of
photovoltaic (PV) systems, an accurate forecasting of PV output is required, which is an
important function in the operation of an energy management system (EMS) for distributed
energy resources. In this paper, a weather-based hybrid method for 1-day ahead hourly
forecasting of PV power output is presented. The proposed approach comprises classification,
training, and forecasting stages. In the classification stage, the self-organizing map(SOM) and
learning vector quantization (LVQ) networks are used to classify the collected historical data of
PV power output.
The training stage employs the support vector regression (SVR) to train the input/output
data sets for temperature, probability of precipitation, and solar irradiance of defined similar
hours. In the forecasting stage, the fuzzy inference method is used to select an adequate trained
model for accurate forecast, according to the weather information collected from Taiwan Central
Weather Bureau (TCWB). The proposed approach is applied to a practical PV power generation
system. Numerical results show that the proposed approach achieves better prediction accuracy
than the simple SVR and traditional ANN methods
Existing system
The existing method are
1) the indirect forecasting methods
2) direct forecasting methods.
For the indirect methods, solar irradiance is predicted based on historical solar irradiance
and weather data, and then is converted to PV power output. The techniques used include fuzzy
logic method wavelet analysis, artificial neural network (ANN), and hybrid ANN-based
methods. The direct methods predict PV power output according to their historical data and
associated weather information. The techniques used consist of support vector regression (SVR)
,ANN, and hybrid ANN methods. These methods as mentioned above are useful for PV power
output forecasting. However, the fuzzy logic-based method cannot learn directly from historical
data. The limitations of wavelet analysis are the complex model structure and the high
computational cost required. Moreover, ANN-based methods can be used for all the
classification and forecasting problems, but require the user to specify various parameters of the
model, especially those related to the network topology. As a result, their performance obtained
is still not satisfied.
Proposed system
This paper presents an alternative approach combining a self organizing map (SOM) , a
learning vector quantization (LVQ) network, the SVR method, and the fuzzy inference approach
to make 1-day ahead hourly forecasting of PV power generation. The SOM transforms input
patterns to a two-dimensional map of features in a topological-ordered fashion. With a simple
structure and efficient learning, the LVQ can effectively classify the input vectors based on
vector quantization. The SVR has a good learning ability and accurate prediction, even when
there are few training samples used.
The fuzzy inference method is used to select a proper SVR sub model for more accurate
predictions. The proposed method comprises classification, training, and forecasting stages. In
the classification stage, the SOM and LVQ networks are used to classify the collected historical
input data. A number of SVR models are then used to learn the collected historical input/output
data sets in the training stage. As the SVR models are trained properly, a fuzzy inference method
is used to select an appropriate model among the trained SVR models to achieve more accurate
forecasting.
Advantage
 improve system reliability,
 maintain power quality,
 Increase the penetration level of the PV power generation system.
The PV power generation has a close relationship with weather conditions, such as the
temperature, solar irradiance, hourly solar angle, and the geographical location. The training
stage employs the support vector regression (SVR) to train the input/output data sets for
temperature, probability of precipitation, and solar irradiance of defined
similar hours.To more accurately capture the weather conditions for every 3 h of the next
day to predict the PV power output, the fuzzy inference method is used to select a proper SVR
submodel in the forecasting stage based on all the available weather predictions, like the
maximum temperature and probability of precipitation, The real PVpower generation data and
irradiance are sampled every 5 min. The hourly PV power output and irradiance data are then
obtained by averaging the data collected within 1 h.
The temperature sensor is read analog signal and is converted through ADC
The sampling times of temperature and probability of precipitation are all in the scale of
1 h. Since the solar irradiance is available from 6:00 to 19:00 in summer season and from 7:00 to
17:00 in non-summer seasons, the daily PV output data are collected only during these intervals.
In the data collection process, the hourly PV power output data may be lost due to the recording
errors or other special events. If abnormal data are collected, the training of SVR or ANN tends
to be unstable. In this paper, the missing data are filled in using the data of same hours on the
latest
similar
day.The
lcd
display
the
13. Hourly foreeasting of PV power output
Solar
Pannel
array
+
-
Solar
charge
ctroller
+
DC load
Power
driven
ckt
battery
A
LCD Display
Micro
controller
Power
supply
Temperature
sensor
status
of
PV
power
output.