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All Journal Jurnal Mahasiswa TEUB Jurnal EECCIS
Mardotillah, Nanda Azizah
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Education Engineering

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ANALISIS SETTING RENEWABLE FRACTION PADA DESAIN PEMBANGKIT LISTRIK TENAGA HIBRID (BAYU-SURYA) - STUDI KASUS DI DESA NGIJO KABUPATEN MALANG Mardotillah, Nanda Azizah; Wibawa, Unggul; Hasanah, Rini Nur
Jurnal Mahasiswa TEUB Vol. 12 No. 4 (2024)
Publisher : Jurnal Mahasiswa TEUB

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Abstract

Electrical energy is essential in modern life, with per capita electricity consumption in Indonesia continuing to rise as the economy and urbanization grow. While reflecting positive economic development, this also puts pressure on electricity infrastructure, demanding sustainable electricity generation alternatives. Indonesia has great potential for wind and solar energy, yetutilization is still limited. Ngijo Village in Malang Regency is an example of hybrid power plant implementation, but its success depends on settings such as Renewable Fraction (RF). This study evaluates the potential of renewable energy in Ngijo Village in 2023 using HOMER simulation. Data on solar radiation, wind speed, system component costs, and the number of families usingelectricity were used to determine the RF setting. Results show Ngijo Village has a large solar radiation potential, with the highest average of 6.26 kWh/m²/day, and wind speeds of 1-2 m/s. Higher RF increases production from the Hybrid Power Plant (PLTH) and decreases production from the public grid. RF also affects the economics of the plant, with net present cost and annualized cost increasing as RF increases, while cost of energy decrease. However, at 60% RF, the cost of energy increase again. Keywords: Setting Renewable Fraction, PLTH, Wind, Solar, HOMER, Ngijo Village
DE-Optimized Hybrid ARIMA-LSTM for Long Term Electricity Load Forecasting Mardotillah, Nanda Azizah; Hasanah, Rini Nur; Wijono
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 2 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i2.1813

Abstract

Accurate long-term electricity load forecasting was essential for efficient energy planning and infrastructure development. This study addressed forecasting challenges in rapidly growing regions, such as East Java, where electricity demand was influenced by both linear and non-linear patterns. Conventional forecasting models, such as the Autoregressive Integrated Moving Average (ARIMA) effectively captured linear trends but failed to model non-linear dynamics, whereas networks with Long Short-Term Memory (LSTM) excelled with non-linear data but were often less effective when used alone. This research developed and evaluated an ARIMA-LSTM hybrid model optimized with the Differential Evolution (DE) algorithm to forecast electricity load until 2026. The model was trained and validated using historical daily load data from 2021 to 2023 from PT PLN UP2B East Java. This hybrid methodology first used ARIMA to model the linear components of the time series. The resulting residual errors, which contained non-linear patterns, were then modeled using an LSTM network. The DE algorithm was used to automatically optimize hyperparameters for both the ARIMA (p, d, q) and LSTM (units, learning rate, drop out etc.) components. The suggested hybrid model's performance was contrasted with that of the independent LSTM and ARIMA models. The results showed that the DE-optimized hybrid model achieved higher accuracy, yielding a Mean Absolute Percentage Error (MAPE) of 3.97 %, which was significantly better than the ARIMA model (12.39 % MAPE) and the LSTM model (4.50 % MAPE) on the validation set. According to these results, the suggested hybrid model was a dependable and extremely accurate instrument for predicting long-term loads, offering a solid basis for strategic energy planning.
Co-Authors Rini Nur Hasanah Unggul Wibawa Wijono