ARTIFICIAL NEURAL NETWORK FOR LOAD FORECASTING IN SMART GRIDHAO-TIAN ZHANG, FANG-YUAN XU, LONG ZHOUEnergy System Group,City University London,Northampton Square ,London,UKE-MAIL: abhbcity.ac.uk, abcx172city.ac.uk, long.zhou.1city.ac.ukAbstract: 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 representation of the ANN applied in load forecast based on practical situation in Ontario Province, Canada.Keywords:Load forecast; Artificial Neuron Network; back propagation training; Matlab1. Introduction Load forecasting is vitally beneficial to the power system industries in many aspects. As an essential 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 season 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. Since 1990, the artificial neural network (ANN) has been researched 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 aneffective way for load forecast in Ontario.2. Back Propagation Network2.1. Background Because 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 select 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 used in neural networks, which is a feed-forward network with continuously valued functions and supervised learning. It can match the input data and corresponding output in an appropriate way to approach a certain function which is used for achieving an expected goal with some previous data in the same manner of the input.2.2. Architecture of back propagation algorithm Figure 1 shows 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 activationfunction 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 selected as the architecture, because this kind of architecture can approximate any function with a few discontinuities. The architecture with three layers is shown in Figure 2 below: Figure 1. Neuron model of back propagation algorithm Figure 2. 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 Figure 3 below. Figure.3. Activation functions applied in back propagation (a)Log-sigmoid (b)Tan-sigmoid (c)linear function2.3. Training function selectionAlgorithms of training function employed based on back propagation approach are used and the function was integrated in the Matlab Neuron network toolbox. TABLE.I. TRAINING FUNCTIONS IN MATLABS NN TOOLBOX3. Training Procedures3.1. Background analysis The neural network training is based 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 weat