Implementation of Mamdani and Sugeno Method for Load Forecasting: A Case Study of Malang City

The growth of energy consumption in many developing countries has exceeded the projection, and therefore, the uncertainty of energy forecasting increases. Variables such as economic growth, population, and efficiency standards, coupled with other factors inherent in the mathematical progression of forecasting models, make accurate projections difficult. The objective of load forecasting is to predict the electrical power required to meet short, medium, or long-term demand, power consumption planning and operations. Large energy consumption over a period of time should be predicted and calculated specifically in order to plan and manage the operation of the power plant. Load forecasting allows utility companies to plan well for future consumption or load demand and also minimize risks for utility companies. Therefore, an accurate method is needed to forecast loads, such as accurate models that take into account factors that affect load growth over several years. In this paper, Mamdani and Sugeno's methods for predicting electrical load forecasting are implemented. The supporting data used in this paper was adopted from Polehan and Blimbing Substations from 2011- 2020. As a result, the average MAPE value according to the Mamdani and Sugeno methods are respectively 2.49% and 1.16%. It can be concluded that the Sugeno method fulfilled the load forecasting from Mamdani