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Regional Forecasts of Photovoltaic Power Generation According to Different Data Availability Scenarios: A Study of 4 Methods
http://hdl.handle.net/2261/00076318
http://hdl.handle.net/2261/000763182197e9e2-9baa-4a98-994b-9f72941211dd
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
PiP_format-Rep.pdf (2.1 MB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||
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| 公開日 | 2018-10-10 | |||||
| タイトル | ||||||
| タイトル | Regional Forecasts of Photovoltaic Power Generation According to Different Data Availability Scenarios: A Study of 4 Methods | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | photovoltaic systems | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | regional power generation | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | support vector regression | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | stratified sampling | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | principal component analysis | |||||
| 資源タイプ | ||||||
| 資源 | http://purl.org/coar/resource_type/c_6501 | |||||
| タイプ | journal article | |||||
| 著者 |
Fonseca, Joao Gari da Silva Junior
× Fonseca, Joao Gari da Silva Junior× Oozeki, Takashi× Ohtake, Hideaki× Takashima, Takumi× Ogimoto, Kazuhiko |
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| 著者所属 | ||||||
| 著者所属 | National Institute of Advanced Industrial Science and Technology | |||||
| 著者所属 | ||||||
| 著者所属 | Institute of Industrial Science (IIS), Collaborative Research Center for Energy Engineering (CEE), Tokyo University | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | The development of methods to forecast PV power generation regionally is of utmost importance to support the spread of such power systems in current power grids. The objective of this study is to propose and to evaluate methods to forecast regional PV power one-day ahead of time and to compare their performances. Four forecast methods were regarded of which 2 are new ones proposed in this study. Together they characterize a set of forecast methods that can be applied in different scenarios regarding availability of data and infrastructure to make the forecasts. The forecast methods were based on the use of support vector regression and weather prediction data. Evaluations were done for 1 year of hourly forecasts using data of 273 PV systems installed in 2 adjacent regions in Japan, Kanto and Chubu. The results show the importance of selecting the proper forecast method regarding the region characteristics. For Chubu, the region with a variety of weather conditions, the forecast methods based on single systems’ forecasts and the one based on stratified sampling provided the best results. In this case the best annual normalized RMSE and MAE were 0.25 kWh/kWhavg and 0.15 kWh/kWhavg. For Kanto, with homogeneous weather conditions, the 4 methods performed similarly. In this case, the lowest annual forecast errors were 0.33 kWh/kWhavg for the normalized RMSE and 0.202 kWh/kWhavg for the normalized MAE. | |||||
| 書誌情報 |
Progress in Photovoltaics 巻 23, 号 10, p. 1203-1218, 発行日 2015-10 |
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| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 1062-7995 | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 1099-159X | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | info:doi/10.1002/pip.2528 | |||||
| 権利 | ||||||
| 権利情報 | Copyright © 2014 John Wiley & Sons, Ltd. | |||||
| 著者版フラグ | ||||||
| 値 | author | |||||
| 出版者 | ||||||
| 出版者 | John Wiley & Sons | |||||
| 異版である | ||||||
| 関連タイプ | isVersionOf | |||||
| 識別子タイプ | URI | |||||
| 関連識別子 | https://doi.org/10.1002/pip.2528. | |||||
