ARTIFICIAL NEURAL NETWORK FOR LOAD FORECASTING
IN SMART GRID
HAO-TIAN ZHANG,FANG-YUAN XU,LONG ZHOU
Energy System Group,City University London,Northampton Square,London,UK E-MAIL:abhb@city.ac.uk,abcx172@city.ac.uk,long.zhou.1@city.ac.uk Abstract:
It is an irresistible trend of the electric power improvement for developing the smart grid,which applies a large amount of new technologies in power generation,transmission,distribution and utilization to achieve optimization of the power configuration and energy saving.As one of the key links to make a grid smarter,load forecast plays a significant role in planning and operation in power system.Many ways such as Expert Systems,Grey System Theory,and Artificial Neural Network(ANN)and so on are employed into load forecast to do the simulation.This paper intends to illustrate the reprentation of the ANN applied in load forecast bad on practical situation in Ontario Province,Canada.
Keywords:Load forecast;Artificial Neuron Network;back propagation training;Matlab
1.Introduction
Load forecasting is vitally beneficial to the power system industries in many aspects.As an esntial part in the smart grid,high accuracy of the load forecasting is required to give the exact information about the power purchasing and generation in electricity market,prevent more energy from wasting and abusing and making the electricity price in a reasonable range and so on.Factors such as ason differences,climate changes,weekends and holidays,disasters and political reasons, operation scenarios of the power plants and faults occurring on the network lead to changes of the load demand and generations.
智商最高是多少>医保卡的作用Since1990,the artificial neural network(ANN)has been rearched to apply into forecasting the load.“ANNs are massively parallel networks of simple processing elements designed to emulate the functions and structure of the brain to solve very complex problems”.Owing to the transcendent characteristics,ANNs is one of the most competent methods to do the practical works like load forecasting.This paper concerns about the behaviors of artificial neural network in load forecasting.Analysis of the factors affectingthe load demand in Ontario,Canada is made to give an
effective way for load forecast in Ontario.
2.Back Propagation Network
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2.1.Background
Becau the outstanding characteristic of the statistical and modeling capabilities,ANN could deal with non-linear and complex problems in terms of classification or forecasting.As the problem defined,the relationship between the input and target is non-linear and very complicated.ANN is an appropriate method to apply into the problem to forecast the load situation.For applying into the load forecast,an ANN needs to lect a network type such as Feed-forward Back Propagation, Layer Recurrent and Feed-forward time-delay and so on.To date,Back propagation is widely ud in neural networks,which is a feed-forward network with continuously valued functions and supervid learning.It can match the input data and corresponding output in an appropriate way to approach a certain function which is ud for achieving an expected goal with some previous data in the same manner of the input.
2.2.Architecture of back propagation algorithm
Figure1shows a single Neuron model of back propagation algorithm. Generally,the output is a function of the sum of bias and weight multiplied by the input.The activation
function could be any kinds of functions.However,the generated output is different.
Owing to the feed-forward network,in general,at least one hidden layer before the output layer is needed.Three-layer network is lected as the architecture,becau this kind of architecture can approximate any function with a few discontinuities.The architecture with three layers is shown in Figure2below:
Figure1.Neuron model of back propagation algorithm
主要事迹简介樱美林大学Figure2.Architecture of three-layer feed-forward network
Basically,there are three activation functions applied into back propagation algorithm,namely,Log-Sigmoid,Tan-Sigmoid,and Linear Transfer Function.The output range in each function is illustrated in Figure3below.
Figure.3.Activation functions applied in back propagation女生的发型
(a)Log-sigmoid(b)Tan-sigmoid(c)linear function
2.3.Training function lection
Algorithms of training function employed bad on back propagation approach are ud and the function was integrated in the Matlab Neuron network toolbox.
TABLE.I.TRAINING FUNCTIONS IN MATLAB’S NN TOOLBOX
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3.Training Procedures
3.1.Background analysis
The neural network training is bad on the load demand and weather conditions in Ontario Province,Canada which is located in the south of Canada.The region in Ontario can be divided into three parts which are southwest,central and east,and north,according to the weather conditions.The population is gathered around southeastern part of the entire province,which includes two of the largest cities of Canada, Toronto and Ottawa.
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3.2.Data Acquisition
The required training data can be divided into two parts:input vectors and output targets.For load forecasting,input vectors for training include all the information of factors
affecting the load demand change,such as weather information,holidays or working days,fault occurring in the network and so on.Output targets are the real time load
scenarios,which mean the demand prented at the same time as input vectors changing.
Owing to the conditional restriction,this study only considers the weather information and logical adjustment of weekdays and weekends as the factors affecting the load
status.In this paper,factors affecting the load changing are listed below:
(1).Temperature(℃)
(2).Dew Point Temperature(℃)
(3).Relative Humidity(%)
(4).Wind speed(km/h)
(5).Wind Direction(10)
(6).Visibility(km)
(7).Atmospheric pressure(kPa)
(8).Logical adjustment of weekday or weekend
According to the information gathered above,the weather information in Toronto taken place of the whole Ontario province is chon to provide data acquisition.The data was gathered hourly according to the historical weather conditions remained in the weather stations.Load demand data also needs to be gathered hourly and correspondingly.In this paper,2years weather data and load data is collected to train and test the created network.
3.3.Data Normalization
Owing to prevent the simulated neurons from being driven too far into saturation,all of the gathered data needs to be normalized after acquisition.Like per unit system,each input and target data are required to be divided by the maximum absolute value in corresponding factor.Each value of the normalized data is within the range between-1and+1so that the ANN could recognize the data easily.Besides,weekdays are reprented as1,and weekend are reprented as0.
3.4.Neural network creating
Toolbox in Matlab is ud for training and simulating the neuron network.The layout of the neural network consists of number of neurons and layers,connectivity of layers,activation functions,and error goal and so on.It depends on the practical situation to t the framework and parameters of the
network.The architecture of the ANN could be lected to achieve the optimized result.Matlab is one of the best simulation tools to provide visible windows.Three-layer architecture has been chon to give the simulation as shown in Figure2above.It is adequate to approximate arbitrary function,if the nodes of the hidden layer are sufficient.
Due to the practical input value is from-1to+1,the transfer function of the first layer is t to be tan sigmiod,which is a hyperbolic tangent sigmoid transfer function.The transfer function of the output layer is t to be linear function,which is a linear function to calculate a layer’s output from its net input.There is one advantage for the linear output transfer function:becau the linear output neurons lead to the output take on any value,there is no difficulty to find out the differences between output and target.
The next step is the neurons and training functions lection. Generally,Trainbr and Trainlm are the best choices around all of the training functions in Matlab toolbox
Trainlm(Levenberg-Marquardt algorithm)is the fastest training algorithm for networks with moderate size.However,the big problem
