ELECTRICITY CONSUMPTION PREDICTION SYSTEM USING A RADIAL BASIS FUNCTION NEURAL NETWORK

The observed poor quality of service being experienced in the power sector of Nigeria economy has been traced to non-availability of adequate model that can handle the inconsistencies associated with traditional statistical models for predicting consumers’ electricity need, so as to bridge the gap between the demand and supply of the energy. This research presents Electricity Consumption Prediction System (ECPS) based on the principle of radial basis function neural network to predict the country’s electricity consumption using the historical data sourced from Central Bank of Nigeria (CBN) annual statistical bulletin. The entire datasets used in the study were divided into train, validation and test sets in the ratio of 13:3:4. By the above, 65% of the entire data were used for the training, 15% for validation and 20% for testing. The train data was presented to the constructed models to approximate the function that maps the input patterns to some known target values. The models were also used to simulate both validation and the test datasets as case data on the consistency of results obtained from the training session through the train data. Experimental results showed that RBF network model performs better than equivalent Backpropagation (BP) network models that were compared with it and provides the best platform for developing a forecast system.