パネル画像を活用した降雪地域における太陽光発電の予測精度向上の可能性検討について

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2022/08/01

タイトル(英語): Examination of Possibility of Improving Accuracy on Predicting Photovoltaic Power Generation in Snowy Areas Using Panel Images

著者名: 西田　義人（金沢工業大学），泉井　良夫（金沢工業大学），鈴木　啓太（金沢工業大学），夏梅　大輔（金沢工業大学），田畑　浩数（金沢工業大学）

著者名(英語): Yoshito Nishita (Kanazawa Institute of Techology), Yoshio Izui (Kanazawa Institute of Techology), Keita Suzuki (Kanazawa Institute of Techology), Daisuke Natsuume (Kanazawa Institute of Techology), Hirokazu Tabata (Kanazawa Institute of Techology)

キーワード: 再生可能エネルギー，太陽光発電量予測，ディープラーニング，降雪地域，パネル画像_x000D_　　renewable energy resources，photovoltaic power prediction，deep learning，snowfall area，panel images

要約(英語): After the sequential end of feed-in tariff (FIT), the electricity generated by photovoltaic (PV) power generation system must be consumed by consumers themselves. Since PV power generation volume is easily affected by weather conditions, it is often used in combination with a power storage device to provide a stable power supply using PV power generation system. However, in order to efficiently control the power storage device according to the amount of power supplied by PV power generation system, it is necessary to accurately predict PV power generation volume. In this research, we will propose a method for predicting PV power generation volume by grasping the snow cover condition of the panel based on the panel image taken by the surveillance camera. We will compare and evaluate the prediction of PV power generation using the snow cover predicted from the panel image with related research and confirm the effectiveness of the proposed method.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.142 No.8 （2022） 特集Ⅰ：Smart Cityのためのセンシング/モビリティ/情報通信技術　特集Ⅱ：サステナブル社会実現に向けた情報・システム技術

本誌掲載ページ: 884-893 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/142/8/142_884/_article/-char/ja/