Systematic review of nowcasting approaches for solar energy production based upon ground-based cloud imaging
Nowcasting of solar energy considering clouds is important for photovoltaic solar plants and distributed systems. Clouds present a challenge for modeling, due to constant changes in shape and size, and are dependent on local atmospheric conditions. Several methods are being used for the automatic as...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-01-01
|
| Series: | Solar Energy Advances |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667113122000079 |
| _version_ | 1811298236529377280 |
|---|---|
| author | Bruno Juncklaus Martins Allan Cerentini Sylvio Luiz Mantelli Thiago Zimmermann Loureiro Chaves Nicolas Moreira Branco Aldo von Wangenheim Ricardo Rüther Juliana Marian Arrais |
| author_facet | Bruno Juncklaus Martins Allan Cerentini Sylvio Luiz Mantelli Thiago Zimmermann Loureiro Chaves Nicolas Moreira Branco Aldo von Wangenheim Ricardo Rüther Juliana Marian Arrais |
| author_sort | Bruno Juncklaus Martins |
| collection | DOAJ |
| description | Nowcasting of solar energy considering clouds is important for photovoltaic solar plants and distributed systems. Clouds present a challenge for modeling, due to constant changes in shape and size, and are dependent on local atmospheric conditions. Several methods are being used for the automatic assessment of clouds from the surface to predict solar power generation, assisted by camera, side sensors, etc. During our research we did not find a Systematic Literature Review on this topic. This review is intended to search the related scientific articles to find the state of the art in the area from the period of 2011–2020. We found 65 articles to review after the meta-analysis. We look for the main short-term forecasting methods used. The majority of articles rely on classical statistics approaches based on historical data. Yet recent articles show that this trend might be shifting towards Machine Learning approaches. Our analysis shows that most articles found are based on images captured by fish-eye lenses using a single camera. The most common forecasting techniques are Artificial Neural Networks and Convolutional Neural Networks, with the root mean squared error being the most predominant error metric used for model validation among both classical and Machine Learning approaches. |
| first_indexed | 2024-04-13T06:16:30Z |
| format | Article |
| id | doaj.art-b0b4a228b57a4e0e9d36c72afdb24912 |
| institution | Directory Open Access Journal |
| issn | 2667-1131 |
| language | English |
| last_indexed | 2024-04-13T06:16:30Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Solar Energy Advances |
| spelling | doaj.art-b0b4a228b57a4e0e9d36c72afdb249122022-12-22T02:58:49ZengElsevierSolar Energy Advances2667-11312022-01-012100019Systematic review of nowcasting approaches for solar energy production based upon ground-based cloud imagingBruno Juncklaus Martins0Allan Cerentini1Sylvio Luiz Mantelli2Thiago Zimmermann Loureiro Chaves3Nicolas Moreira Branco4Aldo von Wangenheim5Ricardo Rüther6Juliana Marian Arrais7Corresponding author.; UFSC Federal University of Santa Catarina, Carvoeira, Florianopolis 88054-700Santa Catarina, BrazilUFSC Federal University of Santa Catarina, Carvoeira, Florianopolis 88054-700Santa Catarina, BrazilINPE Brazilian National Institute for Space Research, Av. dos Astronautas, São José dos Campos 12227-010 São Paulo, BrazilUFSC Federal University of Santa Catarina, Carvoeira, Florianopolis 88054-700Santa Catarina, BrazilUFSC Federal University of Santa Catarina, Carvoeira, Florianopolis 88054-700Santa Catarina, BrazilUFSC Federal University of Santa Catarina, Carvoeira, Florianopolis 88054-700Santa Catarina, BrazilUFSC Federal University of Santa Catarina, Carvoeira, Florianopolis 88054-700Santa Catarina, BrazilUFSC Federal University of Santa Catarina, Carvoeira, Florianopolis 88054-700Santa Catarina, BrazilNowcasting of solar energy considering clouds is important for photovoltaic solar plants and distributed systems. Clouds present a challenge for modeling, due to constant changes in shape and size, and are dependent on local atmospheric conditions. Several methods are being used for the automatic assessment of clouds from the surface to predict solar power generation, assisted by camera, side sensors, etc. During our research we did not find a Systematic Literature Review on this topic. This review is intended to search the related scientific articles to find the state of the art in the area from the period of 2011–2020. We found 65 articles to review after the meta-analysis. We look for the main short-term forecasting methods used. The majority of articles rely on classical statistics approaches based on historical data. Yet recent articles show that this trend might be shifting towards Machine Learning approaches. Our analysis shows that most articles found are based on images captured by fish-eye lenses using a single camera. The most common forecasting techniques are Artificial Neural Networks and Convolutional Neural Networks, with the root mean squared error being the most predominant error metric used for model validation among both classical and Machine Learning approaches.http://www.sciencedirect.com/science/article/pii/S2667113122000079NowcastingCloudSolar energyImage processingGroundPhotovoltaic, |
| spellingShingle | Bruno Juncklaus Martins Allan Cerentini Sylvio Luiz Mantelli Thiago Zimmermann Loureiro Chaves Nicolas Moreira Branco Aldo von Wangenheim Ricardo Rüther Juliana Marian Arrais Systematic review of nowcasting approaches for solar energy production based upon ground-based cloud imaging Solar Energy Advances Nowcasting Cloud Solar energy Image processing Ground Photovoltaic, |
| title | Systematic review of nowcasting approaches for solar energy production based upon ground-based cloud imaging |
| title_full | Systematic review of nowcasting approaches for solar energy production based upon ground-based cloud imaging |
| title_fullStr | Systematic review of nowcasting approaches for solar energy production based upon ground-based cloud imaging |
| title_full_unstemmed | Systematic review of nowcasting approaches for solar energy production based upon ground-based cloud imaging |
| title_short | Systematic review of nowcasting approaches for solar energy production based upon ground-based cloud imaging |
| title_sort | systematic review of nowcasting approaches for solar energy production based upon ground based cloud imaging |
| topic | Nowcasting Cloud Solar energy Image processing Ground Photovoltaic, |
| url | http://www.sciencedirect.com/science/article/pii/S2667113122000079 |
| work_keys_str_mv | AT brunojuncklausmartins systematicreviewofnowcastingapproachesforsolarenergyproductionbasedupongroundbasedcloudimaging AT allancerentini systematicreviewofnowcastingapproachesforsolarenergyproductionbasedupongroundbasedcloudimaging AT sylvioluizmantelli systematicreviewofnowcastingapproachesforsolarenergyproductionbasedupongroundbasedcloudimaging AT thiagozimmermannloureirochaves systematicreviewofnowcastingapproachesforsolarenergyproductionbasedupongroundbasedcloudimaging AT nicolasmoreirabranco systematicreviewofnowcastingapproachesforsolarenergyproductionbasedupongroundbasedcloudimaging AT aldovonwangenheim systematicreviewofnowcastingapproachesforsolarenergyproductionbasedupongroundbasedcloudimaging AT ricardoruther systematicreviewofnowcastingapproachesforsolarenergyproductionbasedupongroundbasedcloudimaging AT julianamarianarrais systematicreviewofnowcastingapproachesforsolarenergyproductionbasedupongroundbasedcloudimaging |