PERGAMON
Applied Thermal Engineering 18 (1998) 1121±1128
Arti®cial neural network based electrical load prediction
for food retail stores
D. Datta *, S.A. Tassou
Department of Mechanical Engineering, Brunel University, Uxbridge, Middlesex, UB8 3PH, U.K.
Received 20 February 1998
Abstract
It has been shown by a number of investigators that arti®cial neural networks (ANNs) can be more
reliable and e€ective building energy predictors than traditional simulation models. This paper presents
the results from comparisons of the predictive accuracy of two commonly used neural networks
employed for the prediction of the electrical load of a retail food store. The networks used were the
multi-layered perceptron (MLP) and radial basis function (RBF). The MLP network was found to
perform better than the RBF network particularly in the prediction of ¯uctuations of the electrical
energy around the base and maximum loads. Further work will be carried out to optimise the structure
and prediction accuracy of the two networks. # 1998 Elsevier Science Ltd. All rights reserved.
Keywords: Electrical load prediction; Retail food stores; Arti®cial neural networks
1. Introduction
Retail food stores are amongst the greatest single end users of electricity with refrigeration
systems accounting for more than 50% of the electricity used. Lighting accounts for about
25% with the Heating, Ventillation and Air Conditioning (HVAC) equipment and other
utilities accounting for the remainder. The retail industry continues to increase the average
store size while upgrading facilities to improve service, reliability, energy eciency and cost
e€ectiveness. The energy consumption of retail store refrigeration systems is a function of a
number of variables which include the building fabric, the ambient conditions (temperature,
solar insolation and wind velocity), the occupancy of the store (i.e. sales activity), and the
internal environment. In the U.K., it is a common practice for the refrigeration and HVAC
system to be part of an integrated design to take advantage of the rejected heat from the
* Corresponding author.
1359-4311/98/$19.00 # 1998 Elsevier Science Ltd. All rights reserved.
PII: S 1 3 5 9 - 4 3 1 1 ( 9 8 ) 0 0 0 3 4 - 9
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refrigeration packs. In trying to minimise energy consumption, therefore, the various energy
consuming subsystems cannot be viewed in isolation but their interactions should be
considered as well as their in¯uence on the sales revenue and pro®tability of the store.
The recent introduction of computer based monitoring and control systems provides the
opportunity not only to characterise the various energy consuming processes in the store but to
relate consumption patterns to fuel pricing and tari€ structures.
Retail food stores, being one of the largest single end users of electricity, qualify to join the
competitive power market in the U.K. The power generating companies have established a
pool from which the supermarkets purchase power on a half hourly basis. The half hourly rate
paid by the supermarket owner to the supplier is to a large extent dependent on his ability to
predict accurately the maximum half-hourly demand and the competitive rates o€ered by
various suppliers. If the actual consumption exceeds the predicted value, the purchaser is
penalised for the extra supply needed by paying at a higher than the negotiated rate. The
ability, therefore, to predict the power consumption every half hour as accurately as possible
will facilitate negotiations on electricity tari€s with the suppliers and will also enable the
control of maximum demand by shifting some of the load to periods of reduced demand.
2. Electrical load prediction using neural networks
Recent approaches to building energy prediction have been based on statistical or numerical
modelling of historical data. In the absence of fast and accurate theoretical models, regression
techniques have been employed to ®nd an approximate functional form that can best describe
the relationship between the independent variables and the observed dependent quantities of
the system of interest [1±3]. When successful, the result is an empirical model that is useful for
predicting the e€ect of changing input data on the dependent variables. The success of
regression techniques largely depends on how well the function developed based on the
available data, mimics the underlying functional relationship of the data. Hence much e€ort
may be spent searching for a suitable function. In addition, for each trial function the
parameters must be optimised before the quality of ®t can be determined. This trial and error
method may be avoided by the use of neural networks.
Arti®cial neural networks are an attempt to recreate simple biological networks by joining
together ``cells'' or ``nodes'' in a cascaded fashion, all richly connected to each other. When a
given set of cells (the inputs) are stimulated, the signals are passed through the network from
node to node and ®nally exit the network through another set of simpli®ed nodes (the
outputs). Any given node accepts input from a number of other nodes, then outputs a signal
based on the sum of all the inputs. Each node is connected to other nodes through a series of
weighting factors by which its output signals can be simpli®ed or attenuated. The trick to
``training'' a network is to ®nd weights such that a given set of inputs causes the network to
yield the desired output. One such learning algorithm is called back-propagation, whereby the
weights are adjusted to reduce the error between the actual and desired outputs of the network.
Detailed descriptions of di€erent network con®gurations and training techniques are given by
Rumelhart and McClelland in ref. [4] and Wasserman in ref. [5] among many others.
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Arti®cial neural networks (ANNs) have been applied successfully to a number of engineering
problems [6, 7]. Several researchers have demonstrated that they can be more reliable at
predicting energy consumption in buildings than other traditional statistical approaches because
of their ability to model nonlinear patterns [8±10]. Neural networks learn the main
characteristics of a system through an iterative training process. They can also automatically
update the learned knowledge on-line over time. This automatic learning facility makes neural
network based systems inherently adaptive. Furthermore, their predictive capability can be used
to optimise the operation of refrigeration, heating and ventilation systems within the building.
Earlier papers by the authors have reported results from the use of a multi-layered
perceptron (MLP) network to predict the overall electrical power consumption of a retail food
store [11, 12]. Inputs to the network were: day of the week, time of day and indoor and
outdoor temperature and humidity. This paper considers the use of the radial basis function
(RBF) network for the electrical energy prediction application and compares its performance
with that of the MLP network.
2.1. The multi-layer perceptron
The multi-layer perceptron, or MLP, is the most popular type of neural network currently in
existence. The MLP consists of a number of simple processing elements arranged in layers. The
inputs to each processing element are actually fully connected to the outputs of the previous layer.
The learning algorithm modi®es the weights associated with each processing element such
that the system minimises the error between the target output and the network's actual output.
At least one hidden layer is required to perform non-linear mappings. The number of
processing elements or nodes in the system, should be directly related to the complexity of the
system being modelled. Although many layered architecture can be applied, it has been shown
that one hidden layer is usually sucient to solve many problems.
The MLP has been shown to be e€ective on a wide range of problems. It is capable of
interpolating and generalising well. However, it may require a considerable length of time to
train, and it does not guarantee ®nding the best global solution.
The parameters usually considered when developing a MLP are the number of hidden nodes
and the learning algorithm. The greater the number of hidden nodes available in the model,
the more complex the function that the system can model. However, if there are too many
hidden nodes of the problem, the network does not ®nd a general solution, but becomes too
speci®c or overtrained. Each problem has an optimal number of hidden layers. Determining
the optimum number depends on the speci®c problem. Hence, developing a MLP involves a
degree of experimentation. Fig. 1 shows a schematic representation of a MLP network. The
activation function through which the sum of the product of the inputs and weights are passed
can be sigmoidal, linear or hyperbolic tan.
2.2. The radial basis function network
The radial basis function network (RBF) is also a supervised, feed-forward neural network
with one hidden layer of nodes. It can be used for the same types of problems as the MLP but
di€ers from perceptron-based networks in two ways.
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Fig. 1. Schematic representation of a multi-layered perceptron network.
Firstly, the outputs that form the hidden layer are not simply the product of the input data
and weights but are the measure of how far away the data are from a centre. This centre is the
position of the node in a spatial system that is de®ned by the input ®elds of the data,
sometimes known as data space.
Secondly, the transfer functions of the nodes are governed by non-linear functions that can
be said to be an approximation of the in¯uence that data points have at the centre. Transfer
functions dictate the level of output from a node and replace the threshold with an output that
varies with the input. The transfer functions used are known as radial basis functions. This
results in a linear combination of non-linear basis functions.
The advantage of the RBF is that the training is much more rapid than with the MLP and
the RBF can model locally clustered data more readily than a MLP. The weaknesses of the
RBF is its poor ability to represent global properties of the data, and the diculty of
determining the optimal positions of the function centres. The parameters which need to be
considered when developing a RBF network are the number of centres required to model the
data accurately, the positioning of the centres and the type of radial function.
The number of centres is highly dependent on the complexity of the problem. Too few
centres result in poor performance. Too many centres result in over-®tting the data and poor
generalisation. The optimum number of centres is determined experimentally.
The non-linear basis function de®nes the form of the receptive ®eld associated with each
node. The shape of the function dictates how the node responds to unseen data points. The
original RBF used a Gaussian basis function. However, there are many other functions namely
spline, multi-quadratic and inverse multi-quadratic which can be used.
2.3. Experimental set-up and monitoring
The investigations for this project are based on a retail food store situated in Airdrie, UK.
This store is equipped with a commercial central monitoring and control system which
monitors the temperatures in the display cabinets in the store and controls the refrigeration
packs. For the purposes of the project the system has been extended to incorporate a number
of additional measuring points which include:
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1125
temperature and relative humidity in the store;
external air temperature and humidity;
solar irradiance;
total electrical power consumption of the store;
electrical power consumption of the refrigeration packs;
gas consumption;
under¯oor heating ¯ow and return temperatures;
instantaneous store occupancy (shopping activity).
All the above data is logged every 15 min through a modem on a personal computer at Brunel
University, for subsequent analysis.
2.4. Training and testing of neural networks
Data collected from the retail store was used to train, validate and test the two networks.
The input data employed were: day of the week, time of the day, outdoor temperature and
humidity and indoor temperature and humidity. The training data employed were for a period
of 2 months, and the trained networks were tested for prediction accuracy on the following 6
days.
The MLP network used was three layered with six nodes on the input layer, four nodes on
the single hidden layer and one node on the output layer. The transfer function used was the
hyperbolic tan and the algorithm used was back-propagation [4]. The RBF network also had
six nodes on the input layer. The number of centres was varied between ®ve and 50 in
increments of ®ve, and the best network con®guration according to the validation percentage
was stored and used as the best network. The nonlinear basis function used was the spline.
3. Results and discussion
Figs. 2 and 3 show a comparison of the outputs of the two networks and the actual value of
power consumed while Table 1 presents the error values. The network outputs are on the test
data, that is, data not used to train the networks.
The MLP network output in Fig. 2 shows a close ®t to the actual data. The network
predicts fairly accurately the minimum and maximum electrical loads in the store. It can also
predict some of the ¯uctuations in load that take place because of on/o€ switching of the
electrical equipment.
The RBF network output, shown in Fig. 3, follows the general load pro®le, but tends to
overpredict the maximum load and underpredict the base load of the store. The RBF network
also does less well in capturing the load ¯uctuations that take place around the maximum and
base load.
The error values for the two networks are shown in Table 1. It can be seen that the mean
absolute error value for the MLP network output is much lower than that of the RBF network
output which con®rms the better performance of the MLP in this case. However, for both
networks it may be possible to improve their prediction accuracy by optimising their structure
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Fig. 2. Comparison between predicted and actual data over a 6 day period using the multi-layered perceptron
network.
Fig. 3. Comparison between predicted and actual data over a 6 day period using the radial basis function network.
D. Datta, S.A. Tassou / Applied Thermal Engineering 18 (1998) 1121±1128
1127
Table 1
Output error measures
Output
MLP network
RBF network
RMS error
24.19
34.92
Mean absolute
17.38
26.92
Mean absolute (%)
4.59
7.11
and the input variables. As mentioned earlier, the identi®cation of the most appropriate
network and network structure is purely a matter of experimentation and trial and error.
4. Conclusions
Results in this paper show that a simple neural network structure can provide prediction of
the electrical load of a retail food store with a reasonable degree of accuracy. It can also be
seen that provided the inputs to the network are properly selected, arti®cial neural networks
can trace the instantaneous load ¯uctuations around the peak and base loads to some extent.
It has also been shown that the MLP network in the form used in the investigations can
provide better prediction accuracy than the RBF network. Further investigations need to be
carried out to enhance the prediction accuracy of the networks by introducing store opening
characteristics and subsystem ANN modelling into the overall system model.
Once an optimum ANN based predictor has been developed the network can be used for online performance analysis and system diagnostics which may include identi®cation of
malfunctions in equipment, maintenance requirements etc. Simple neural nets can be
implemented on existing computer based monitoring and control systems at very little extra cost.
Acknowledgements
The authors would like to acknowledge the EPSRC for funding this project and Safeway
Stores PLC and Elm Ltd who are the industrial collaborators.
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