On the Use of an Ensemble-based Approach to Improve Forecast of Photovoltaic Power Generation with Support Vector Machines

カテゴリ: 全国大会

論文No: 6-104

グループ名: 【全国大会】平成26年電気学会全国大会論文集

発行日: 2014/03/05

タイトル(英語): On the Use of an Ensemble-based Approach to Improve Forecast of Photovoltaic Power Generation with Support Vector Machines

著者名: ガリ ダシルバフォンセカジュニアジョン(産業技術総合研究所),大関 崇(産業技術総合研究所),大竹 秀明(産業技術総合研究所),髙島 工(産業技術総合研究所),荻本 和彦(東京大学)

著者名(英語): Gari da Silva Fonseca Junior Joao (AIST),Oozeki Takashi(AIST),Ohtake Hideaki(AIST),Takashima Takumi(AIST),Ogimoto Kazuhiko(Tokyo University)

キーワード: サポートベクターマシン|太陽光発電電力|予測手法の設定|アンサンブル手法

要約(日本語): In a previous case study it was shown that an ensemble-based approach, using only the training data, can yield forecast accuracies as high as the ones obtained with 1 year of spare data and the grid search in the problem of insolation forecasting. In this study we extend such application for the PV power forecasting problem evaluating the technique in hourly forecasts for 586 PV systems in 2009 and 756 PV systems in 2010. The results validated the use of the approach. They showed that in average more than 8% of reduction of the annual absolute forecast error can be achieved with the proposed approach. Regarding the rmse the reduction was smaller, 3.25%, but still consistent.

原稿種別: 日本語

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