A Study On Short Term Load Forecasting Using Back Propagation Neural Network

Abdul Hasif, Abdul Halim (2010) A Study On Short Term Load Forecasting Using Back Propagation Neural Network. Project Report. UTeM, Melaka, Malaysia. (Submitted)

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Abstract

Artificial Neural Networks (ANN) has been applied to many fields in recent years. Among them, the neural networks with Back Propagation algorithm appear to be most popular and have been widely used in applications such as forecasting and classification. This project is to predict or forecast the load flow for economic dispatch by using ANN. The main objective of this project is to develop ANN model that will give a faster result .compared with conventional method. The load forecasting is an important component in the operation and planning of electrical power generation. In order to minimize the operating cost, an electrical supplier will use a forecasted demand to control the number of running generator units. The short-term load forecasting (STLF) provides load data in hourly forecasting and it is important for daily maintenance of power plant. This project will predict future load data is peninsular Malaysia. The input that will use for forecasted are is half hourly load data for seven weeks and the actual load data that used for compared are was loaded data fkom eight week. The end of this project, the result between forecasted load data using Matlab will compared with actual load data to get the error and to achieve the minimum forecasting error.

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