Load Forecasting Using Fuzzy Logic, ANN, and ANFIS Approaches: Application to south-western Morocco

Authors: Hicham Stitou, Mohamed amine Atillah, Abdelghani Boudaoud, Mounaim Aqil (IJECE ID 36303)

The demand for energy on a global scale is continuously rising due to the expansion of energy infrastructure and the increasing number of new appliances. To address this growing need, an efficient energy management system (EMS) has become indispensable. By implementing EMS, both residential and commercial buildings can significantly improve their energy efficiency and consumption. Load forecasting is a critical aspect of enabling EMS to operate efficiently. The accuracy of load forecasting depends on a variety of factors. A reliable load forecast model should take into account the region's weather forecast, which is critical in developing an accurate prediction. This study is about the medium-term load forecasting (MTLF) for the province of Taroudant, Morocco, using historical monthly load and weather data for five years (2018–2022). We use three methods to forecast consumed energy: artificial neural networks (ANN), fuzzy logic (FL), and adaptive neuro-fuzzy inference systems (ANFIS). This paper selects absolute percentage error (APE), mean absolute percentage error (MAPE), correlation coefficient (R), and root mean square error (RMSE) to compare and evaluate the prediction accuracy of models. The results analysis reveals that the ANFIS model generates very accurate forecasting predictions with a MAPE of 4.75%, whereas the ANN and FL models yield a MAPE of 7.36% and 8.42%, respectively,

International Journal of Electrical and Computer Engineering (IJECE)
https://ijece.iaescore.com

Supported by Master Program of Electrical Engineering, Universitas Ahmad Dahlan, https://mee.uad.ac.id #yogyakarta
Admission: https://mee.uad.ac.id/pendaftaran/

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