Very short-term photovoltaic power forecasting with cloud modeling: A review
This paper endeavors to provide the reader with an overview of the various tools needed to forecast photovoltaic (PV) power within a very short-term horizon. The study focuses on the specific application of a large scale grid-connected PV farm. Solar resource is largely underexploited worldwide wher...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Elsevier Science Ltd
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/50390 |
| _version_ | 1848758464214466560 |
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| author | Barbieri, F. Rajakaruna, Sumedha Ghosh, Arindam |
| author_facet | Barbieri, F. Rajakaruna, Sumedha Ghosh, Arindam |
| author_sort | Barbieri, F. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper endeavors to provide the reader with an overview of the various tools needed to forecast photovoltaic (PV) power within a very short-term horizon. The study focuses on the specific application of a large scale grid-connected PV farm. Solar resource is largely underexploited worldwide whereas it exceeds by far humans' energy needs. In the current context of global warming, PV energy could potentially play a major role to substitute fossil fuels within the main grid in the future. Indeed, the number of utility-scale PV farms is currently fast increasing globally, with planned capacities in excess of several hundred megawatts. This makes the cost of PV-generated electricity quickly plummet and reach parity with non-renewable resources. However, like many other renewable energy sources, PV power depends highly on weather conditions. This particularity makes PV energy difficult to dispatch unless a properly sized and controlled energy storage system (ESU) is used. An accurate power forecasting method is then required to ensure power continuity but also to manage the ramp rates of the overall power system. In order to perform these actions, the forecasting timeframe also called horizon must be first defined according to the grid operation that is considered. This leads to define both spatial and temporal resolutions. As a second step, an adequate source of input data must be selected. As a third step, the input data must be processed with statistical methods. Finally, the processed data are fed to a precise PV model. It is found that forecasting the irradiance and the cell temperature are the best approaches to forecast precisely swift PV power fluctuations due to the cloud cover. A combination of several sources of input data like satellite and land-based sky imaging also lead to the best results for very-short term forecasting. |
| first_indexed | 2025-11-14T09:44:24Z |
| format | Journal Article |
| id | curtin-20.500.11937-50390 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:44:24Z |
| publishDate | 2015 |
| publisher | Elsevier Science Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-503902018-11-30T04:17:32Z Very short-term photovoltaic power forecasting with cloud modeling: A review Barbieri, F. Rajakaruna, Sumedha Ghosh, Arindam This paper endeavors to provide the reader with an overview of the various tools needed to forecast photovoltaic (PV) power within a very short-term horizon. The study focuses on the specific application of a large scale grid-connected PV farm. Solar resource is largely underexploited worldwide whereas it exceeds by far humans' energy needs. In the current context of global warming, PV energy could potentially play a major role to substitute fossil fuels within the main grid in the future. Indeed, the number of utility-scale PV farms is currently fast increasing globally, with planned capacities in excess of several hundred megawatts. This makes the cost of PV-generated electricity quickly plummet and reach parity with non-renewable resources. However, like many other renewable energy sources, PV power depends highly on weather conditions. This particularity makes PV energy difficult to dispatch unless a properly sized and controlled energy storage system (ESU) is used. An accurate power forecasting method is then required to ensure power continuity but also to manage the ramp rates of the overall power system. In order to perform these actions, the forecasting timeframe also called horizon must be first defined according to the grid operation that is considered. This leads to define both spatial and temporal resolutions. As a second step, an adequate source of input data must be selected. As a third step, the input data must be processed with statistical methods. Finally, the processed data are fed to a precise PV model. It is found that forecasting the irradiance and the cell temperature are the best approaches to forecast precisely swift PV power fluctuations due to the cloud cover. A combination of several sources of input data like satellite and land-based sky imaging also lead to the best results for very-short term forecasting. 2015 Journal Article http://hdl.handle.net/20.500.11937/50390 10.1016/j.rser.2016.10.068 Elsevier Science Ltd fulltext |
| spellingShingle | Barbieri, F. Rajakaruna, Sumedha Ghosh, Arindam Very short-term photovoltaic power forecasting with cloud modeling: A review |
| title | Very short-term photovoltaic power forecasting with cloud modeling: A review |
| title_full | Very short-term photovoltaic power forecasting with cloud modeling: A review |
| title_fullStr | Very short-term photovoltaic power forecasting with cloud modeling: A review |
| title_full_unstemmed | Very short-term photovoltaic power forecasting with cloud modeling: A review |
| title_short | Very short-term photovoltaic power forecasting with cloud modeling: A review |
| title_sort | very short-term photovoltaic power forecasting with cloud modeling: a review |
| url | http://hdl.handle.net/20.500.11937/50390 |