SHORT-TERM LOAD FORECASTING USING ANN TECHNIQUE

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 fulfill 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 Multi-Layer 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. Several models were developed and tested on the real load data of a Finnish electric utility. Most of them use a MLP network to identify the assumed relation. We carried out shortterm load forecasting for P.D.V.V.P.COE, Ahmednagar college campus using ANN (Artificial Neural Network) technique ANN was implemented on MATLAB-10. MLP (Multi-layer Perceptions was made with input as days and hourly load. Hourly load means the hourly power consumption in college. Error was calculated as MAPE (Mean Absolute Percentage Error) and with error of about 0.956% this paper was successfully carried out. This paper can be implemented by any intensive power consuming company/ college for predicting the future load and would proved to be very useful tool while sanctioning the load KEYWORD