Medium-Term Load Forecasting Of Covenant University Using The Regression Analysis Methods

Electric load forecasting is principal to the economic and efficient provision of electric power to meet various load demands for a specified period of time. The focus of this paper is on the medium-term load forecast of Covenant University, a Nigerian tertiary educational institution, using the methods of regression analysis. The hourly load information collected from the University’s electric power substation is employed as the sample data for the forecast. Three models based on the linear, compound-growth, and cubic methods of regression analysis are developed, and the load forecast results obtained from these models are compared using the mean absolute percentage error (MAPE) and root mean square error (RMSE) performance metrics. It is observed that, among these three models, the linear model has the best error margin: 0.5792 and 41.34 for MAPE and RMSE, respectively