Short Term Load Forecasting With Feed Forward Neural Network Algorithm

Muhamad Ruslan, Muhamad Fairuz (2010) Short Term Load Forecasting With Feed Forward Neural Network Algorithm. Project Report. UTeM, Melaka, Malaysia. (Submitted)

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Abstract

Load forecasting has become one of the major areas of research in electrical engineering in recent years. Several electric companies are now forecasting load power based on conventional method. However, since the relationship between load power and factor influencing load power is nonlinear, it is difficult to identify its nonlinearity by using conventional method. For this final year project, it involves Short Term Load Forecasting (STLF) with feed forward neural network algorithm. Artificial Neural Network (ANN) has been proved as a powerful alternative for STLF that it is not relying on human experience. This project deals with case study and simulation using Neural Network in Matlab software to forecast load in Peninsular Malaysia. The load data is taken for half hourly load because the aim is to get the minimum error about less or equals to 1.5%.

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