Stanley
SHORT-TERM ELECTRIC POWER FORECAST IN THE NIGERIAN POWER SYSTEM USING ARTIFICIAL NEURAL NETWORK
ABSTRACT
This thesis is a study of short-term electric power forecasting in the Nigerian power system using artificial neural network model. The model is created in the form of a simulation program written with MATLAB tool. The model, a multilayer time delayed feed-forward artificial neural network trained with error back propagation algorithm, was made to study the pre-historical load pattern of a typical Nigerian power system in a supervised training manner. After presenting the model with a reasonable number of training samples, the model could forecast correctly electric power supply in the Nigerian power system 24 hours in advance. An absolute mean error of 4.27% was obtained when the trained neural network model was tested on one week, daily hourly load data of a typical Nigerian power station. This result demonstrates that ANN is a powerful tool for load forecasting.
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