Comparison of Medium-Term Load Forecasting Methods (Splitted Linear Regression and Artificial Neural Networks) in Electricity Systems Located in Tropical Regions

Load forecasting for the medium term to supply power plants within 1 month, can optimize the economic dispatch of generators that will be used to supply large systems. The accuracy of forecasting the half-hourly load will result in a more efficient electricity supply in a system that uses flat rates on the utility side. In this paper, the author tries to compare the forecasting methods of linear regression, Artificial Neural Networks, and Splitted Linear Regression. This method is applied to the largest system in Indonesia, a country located at the tropical region which has different characteristics from countries that have four seasons. At the end of this study, it can be concluded that the Splitted Linear Regression method has the highest performance with the lowest MAPE value of 2.63%.