Wind speed prediction and energy estimation using the SARIMA method in Banyumas Regency
SARIMA法を用いたバニュマス県における風速予測とエネルギー推定 (AI 翻訳)
Yuniarto, Abdul Hakim Prima, Nawangnugraeni, Devi Astri, Admaja, Rafif Aldo, Arsyad, Hardeka Muhammad
🤖 gxceed AI 要約
日本語
本研究は、インドネシア・バニュマス県の風速をSARIMAモデルで予測し、小型風力発電による発電ポテンシャルを評価した。最適モデルSARIMA(1,0,0)×(0,1,1,52)により、3ヶ月後の平均風速3.41m/s、1日あたり1.44kWhの電力量が推定され、小規模風力発電の可能性を示した。
English
This study predicts wind speed in Banyumas Regency, Indonesia using SARIMA models and evaluates electricity potential from small-scale wind turbines. The best model SARIMA(1,0,0)×(0,1,1,52) forecasts average wind speed of 3.41 m/s and daily energy output of 1.44 kWh, indicating feasibility for household-scale wind power.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
In the global GX context
This paper provides a methodological example of wind resource assessment using SARIMA, but its local scope limits direct relevance to global corporate GX disclosure or carbon accounting frameworks.
👥 読者別の含意
🔬研究者:Renewable energy modelers may find the SARIMA application and grid-search approach useful for wind prediction in data-sparse regions.
🏛政策担当者:Local policymakers in Indonesia may reference the feasibility of small-scale wind turbines for rural electrification.
📄 Abstract（原文）
Electricity consumption in Banyumas Regency shows a significant upward trend, indicating growing energy needs across various sectors. Dependence on fossil fuels poses challenges, including environmental pollution, limited resources, and price fluctuations. As a strategic solution, developing new and renewable energy, especially wind energy, is crucial to achieving energy independence and environmental sustainability. This study aims to analyze and predict wind speed in Banyumas Regency and calculate the potential electricity production that residential-scale wind turbines can generate. The method used is the seasonal auto regressive integrated moving average (SARIMA). This study applies it within a machine learning framework, using a grid search for hyperparameter tuning, to accurately predict wind speed from historical NASA POWER data. The results show that the SARIMA (1, 0, 0)×(0, 1, 1, 52) model is the optimal model with the best prediction accuracy, as evidenced by the root mean squared error (RMSE) value of 0.516 m/s and the mean absolute error (MAE) of 0.441 m/s. Based on the model, the predicted average wind speed for the next three months is 3.41 m/s, potentially generating an average daily electricity output of 1.44 kWh. These results indicate that Banyumas Regency has promising potential for the development of small-scale wind power plants to support household energy needs or public street lighting.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/20519831first seen 2026-06-03 04:23:07 · last seen 2026-06-03 04:23:12
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