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p-Index From 2020 - 2025
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All Journal Journal La Sociale Journal La Multiapp
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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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.
Value at Risk Analysis for Asset Acquisition Investment Needs Process Using Monte Carlo Method Based on Asset Return Level Nugroho, Catur Arie; Zulfialda, Dennis Hasnan; Malasan, Hakim Luthfi
Journal La Sociale Vol. 6 No. 2 (2025): Journal La Sociale
Publisher : Borong Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journal-la-sociale.v6i2.1860

Abstract

This study aims to determine the Value at Risk (VaR) analysis process in measuring the maximum level of losses that can be accepted in the investment process to acquire assets that are already operating. An investment program must be able to measure the risks that will be faced in the future so that the VaR analysis. This study uses a quantitative approach through secondary data. The techniques used are data preprocessing scaling techniques, correlation between features and forecasting modeling through linear and non-linear regression mechanisms through repeated simulations (Monte Carlo). The research method for this VaR analysis uses several features from historical data with a probability level of 95%. From the results of the simulation of VaR, a prediction was obtained for the next 3 years, investors will not suffer losses, so that the profit obtained is $ 25 million from the estimated asset return value of 1.3% with a Mean Squared Error (MSE) value of 0.13. Based on the weighting results, it was also found that the asset value volatility parameter has the largest weight that affects the VaR value.
Co-Authors Malasan, Hakim Luthfi Nugroho, Catur Arie