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Zulfialda, Dennis Hasnan
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Engineering Social Sciences

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Journal : Journal La Multiapp

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Prediction of Hydropower Plant Electricity Production Dependence on Weather Conditions Using the SARIMAX Model Zulfialda, Dennis Hasnan; Nugroho, Catur Arie; Malasan, Hakim Luthfi
Journal La Multiapp Vol. 6 No. 1 (2025): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v6i1.1841

Abstract

Electricity production from Hydroelectric Power Plants (PLTA) that depends on the water capacity in the dam. The water capacity depends on uncertain weather conditions such as drought caused by the El Niño Storm, which has an impact on the lack of water supply that enters the hydropower turbine which is then converted into electrical energy. Accurate predictions are needed to be able to mitigate existing weather fluctuations. In this study, the SARIMAX model on electricity production data integrated with weather data for 4 years from January 2020 to December 2023. The SARIMAX model with optimal parameters (p=0, d=1, q=1, P=1, D=0, Q=1, s=12) provides quite satisfactory prediction results for hydropower power production. SARIMAX obtained MSE values of 0.00101, MAE 0.0274, and RMSE 0.0318. The study also highlights the significance of accurate prediction of hydropower production, emphasizing the importance of external factors such as weather in particular El Niño. Understanding and predicting weather patterns is critical to the power generation system of hydropower in making decisions and optimizing the operation of the electricity system efficiently.
Co-Authors Malasan, Hakim Luthfi Nugroho, Catur Arie