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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Bulletin of Electrical Engineering and Informatics
Ricky Maulana
Universitas Andalas
Control & Systems Engineering Electrical & Electronics Engineering

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Solar power forecasting using a SARIMA approach for Indonesia's grid integration Ricky Maulana; Syafii Syafii; Aulia Aulia
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i1.pp293-302

Abstract

Indonesia’s transition toward a renewable energy-dominated power grid is progressing to meet increasing energy demands while reducing dependence on fossil fuels. According to the National Energy General Plan, their goal is to have 23% of the energy mix come from renewables by 2025 and 31% by 2050. Accurate forecasting of photovoltaic (PV) power output is crucial to address the intermittent nature of solar energy and ensure grid stability. A seasonal autoregressive integrated moving average (SARIMA) model was developed to estimate day-ahead photovoltaic power output in Padang City, Indonesia. Using NASA solar irradiance data from March 1-31, 2024, the SARIMA(1,0,1)(4,0,3)24 model achieved high accuracy with an NRMSE of 4.19%. To evaluate its performance, a comparative evaluation was conducted between the SARIMA model and two machine learning methods, namely artificial neural network (ANN) and long short-term memory (LSTM), in which SARIMA achieved the lowest forecasting error. These findings indicate that SARIMA remains an effective and interpretable statistical method for short-term PV forecasting, supporting reliable energy planning and power grid operations towards Indonesia's renewable energy goals.
A framework for multi-interval optimal power flow under solar energy penetration Ricky Maulana; Syafii Syafii; Aulia Aulia
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11091

Abstract

The increasing penetration of renewable energy introduces variability and uncertainty into power system operations, thus requiring accurate forecasting methods to ensure reliable and economical scheduling. This study presents a multi-interval day-ahead optimal power flow (OPF) analysis integrated with photovoltaic (PV) generation, where hourly PV forecasts are obtained using the seasonal autoregressive integrated moving average (SARIMA) (1,0,1)(4,0,3)24 model. The forecast results achieved low error values (root mean square error (RMSE)=0.354, normalized RMSE (NRMSE)=4.192%, mean absolute error (MAE)=0.202), successfully capturing the daily PV generation pattern and providing sufficiently accurate input for the OPF simulation. The forecasted PV profiles were then integrated into a multi-interval OPF framework using the MATPOWER interior point solver (MIPS) solver. Results show that PV integration reduces system operating costs compared to cases without PV, with cost savings observed at various time intervals (e.g., reduction from $802.22/hour to $780.65/hour during PV peak hours). Compared to the conventional single-interval OPF benchmark based on Weibull distribution assumptions for PV, the proposed framework achieves lower average costs ($790.97/hour vs. $869.70/hour) while also reflecting the real variability of solar dynamics and load. Overall, the integrated forecasting-optimization framework demonstrates that SARIMA-based PV forecasting provides reliable inputs for OPF and offers a practical tool to support future system planning and operation with higher renewable energy penetration.
Co-Authors Aulia Aulia Syafii .