日本地球惑星科学連合2023年大会

講演情報

[J] 口頭発表

セッション記号 M (領域外・複数領域) » M-ZZ その他

[M-ZZ40] 再生可能エネルギーと地球科学

2023年5月24日(水) 15:30 〜 16:45 105 (幕張メッセ国際会議場)

コンビーナ:大竹 秀明(国立研究開発法人 産業技術総合研究所 再生可能エネルギー研究センター)、野原 大輔(電力中央研究所)、島田 照久(弘前大学大学院理工学研究科)、宇野 史睦(日本大学文理学部)、座長:島田 照久(弘前大学大学院理工学研究科)


16:00 〜 16:15

[MZZ40-03] 太陽光発電出力予測の不確実性の定量化

*野原 大輔1菅野 湧貴1 (1.電力中央研究所)

キーワード:太陽光発電、予測、不確実性

Renewable energy such as photovoltaic (PV) and wind power have a character of volatility due in part to the time evolution of weather systems. Prediction for the PV power outputs is one of the most cost-effective and easily implemented tools. Despite the recent increase in the accuracy of numerical weather prediction models, there is a limitation to reducing the prediction error. In addition to the improvement of the models, the use of statistical methods such as machine learning is accelerating. In this study, we summarize the causes of the prediction error of the PV and quantification of the uncertainty inherent in the prediction. The uncertainty caused by the chaotic behavior of the atmosphere increases with prediction time, but it is difficult to reduce the uncertainty. On the other hand, systematic uncertainties are caused by imperfections in the models, PV power conversion, and other factors. The uncertainties can be reduced by statistical methods. The ratio of uncertainties in the chaotic behavior and systematic is about 1 to 2 (2 to 3) for the 0-6 (24-30) hours ahead prediction. Systematic uncertainties can be reduced using statistical methods, suggesting that there is still room to reduce the spread of probabilistic predictions and prediction errors.