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Abstrato

Comparison of Regression Model and Artificial Neural Network Model for the prediction of Electrical Power generated in Nigeria

Olaniyi S Maliki, Anthony O Agbo, Adeola O Maliki, Lawrence M Ibeh, Chukwuemeka O Agwu

Energy is the fundamental resource, it gives the ability to transform, transport and manufacture any and all goods and it is vital to the development of any economy. In Nigeria, electricity is one of the oldest energy forms available for daily activities. It is also, unfortunately, grossly inadequate to meet the demands of an ever increasing population. This is largely due to inadequate planning. Efficient energy management necessitates the development and utilization of an energy plan to ensure a balance between demand and supply with any economy. Energy analysis is defined as a particular set of procedures for evaluating the total energy requirements for the supply of a service or project. Energy analysis is an important exercise in the overall energy systems planning and management. Its relevance lies in the generation of forecasts for future energy consumption (demand/supply) patterns, and this is the main objective of the present work. Regression and artificial neural network methods are employed in energy analysis to determine energy requirements up to 2036. We examine in particular the problems of Nigeria’s electricity system and based on electricity generation and consumption data we present a conceptual approach aimed at enhancing electricity generation in the country. The predicted values of the responses by ANN and regression models were compared and their closeness with the actual data values was determined.