Application of the adaptive neuro-fuzzy inference system for prediction of the electrical energy production in Jakarta

Abstract

Jakarta, as a rapidly growing urban area, faces challenges in balancing energy demand with supply while addressing environmental concerns associated with traditional energy sources. Electrical energy production prediction in urban environments like Jakarta is crucial for effective energy management, ensuring stable supply, and promoting sustainable development. The prediction of electrical energy production in Jakarta is critical for ensuring stable and sustainable energy supply. This research proposed the application of the adaptive neuro-fuzzy inference system (ANFIS) as a predictive tool specifically tailored for Jakarta's energy production prediction context. The research methodology used in this study is the ANFIS. Five levels make up the architecture of the ANFIS model: output, normalization, defuzzification, rule evaluation, and fuzzification. The fuzzification layer converts input variables into linguistic terms using membership functions, while the rule evaluation layer calculates the activation strength of each rule based on the input values. The predicted results of Jakarta electrical energy production from 2023 to 2028 are 65,288 GWh and there is an annual increase of 5.25%. The error contained in ANFIS is with a root mean square error (RMSE) value of 0.0001058% and a mean absolute percentage error (MAPE) value of 0.00875%.