Various Transfer Functions of BPN in Electricity Load Forecasting

Load forecasting is the technique for prediction of electrical load. In a deregulated market it is much need for a generating company to know about the market load demand for generating near to accurate power. If the generation is not sufficient to fulfil the demand, there would be problem of irregular supply and in case of excess generation the generating company will have to bear the loss. Neural network techniques have been recently suggested for short-term load forecasting by a large number of researchers. This work studies the applicability of this kind of models. The work is intended to be a basis for a real forecasting application. First, a literature survey was conducted on the subject. Most of the reported models are based on the so-called Multilayer Perceptron (MLP) network. There are numerous model suggestions, but the large variation and lack of comparisons make it difficult to directly apply proposed methods. It was concluded that a comparative study of different model types seems necessary. Back propagation in neural network is used to train neural network. Various techniques are used in BPP analysis. In this paper mean square error (MSE) is considered as performance criteria and various BPP methods are analysed on MSE criteria.