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All Journal Journal of Renewable Energy, Electrical, and Computer Engineering
Yuan Anisa
Department of Informatics, Universitas Amikom Yogyakarta, Yogyakarta, 55283
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Short-Term Forecasting of Electricity Consumption Using Fuzzy Logic Amri M Rizaldi; Ahmad Ridwan; Yuan Anisa; Rudi Salam; Gramandha Wega Intyanto; Daniel T Cotfas; Vo Hung Cuong; Uduak I. Udoudom
Journal of Renewable Energy, Electrical, and Computer Engineering Vol 3, No 2 (2023): September 2023
Publisher : Institute for Research and Community Service, Universitas Malikussaleh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jreece.v3i2.11281

Abstract

The high demand for electricity in the production process at PT Semen Padang requires a system that can cope with various kinds of disturbances. The problem is that the need for electrical loads is dynamic, especially in the short term, allowing fluctuations between electrical loads at uncertain times. A short-term electric energy consumption forecasting method is needed to determine load growth and distributed power supply. This research aims to use a fuzzy logic algorithm to perform short-term electrical energy consumption forecasting and compare the forecasting results with the actual load at PT Semen Padang. The results showed that short-term load forecasting for seven days using the fuzzy Mamdani method, namely Smallest of Maximum (SoM), obtained a percentage MAPE value of 8.15%. Meanwhile, the Weight of Average (WoA) Sugeno defuzzification method gets a portion of the MAPE value of 9.51%. The Sugeno method is more accurate than the Mamdani method in short-term electricity load forecasting for PPI Indarung V PT Semen Padang. If based on the time category, then forecasting the electricity load on holidays is better than predicting on weekdays. However, when viewed in terms of per day, in Wednesday has the smallest average MAPE value of 5.05%.
Co-Authors Ahmad Ridwan Amri M Rizaldi Daniel T Cotfas Gramandha Wega Intyanto Rudi Salam Uduak I. Udoudom Vo Hung Cuong