A Review of Multitemporal and Multispatial Scales Photovoltaic Forecasting Methods
Reliable photovoltaic(PV) forecasting can provide important data support for power system operation, which is the key to realize the large-scale consumption of solar energy resources. PV forecasting task becomes crucial to ensure power system stability and economic operation. This paper reviews the...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9741707/ |
| _version_ | 1819047225562497024 |
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| author | Caicheng Liu Ming Li Yunjun Yu Ziyang Wu Hai Gong Feier Cheng |
| author_facet | Caicheng Liu Ming Li Yunjun Yu Ziyang Wu Hai Gong Feier Cheng |
| author_sort | Caicheng Liu |
| collection | DOAJ |
| description | Reliable photovoltaic(PV) forecasting can provide important data support for power system operation, which is the key to realize the large-scale consumption of solar energy resources. PV forecasting task becomes crucial to ensure power system stability and economic operation. This paper reviews the existing research of PV forecasting methods from the perspective of multi-temporal scale and multi-spatial scale. Firstly, according to the forecasting process, demand, temporal and spatial scale, the forecasting methods are classified and the evaluation indicators involved in the research are listed. Secondly, based on the temporal scale of PV power generation, the results are combed through the three kind of scale of ultra-short-term, short-term and medium and long-term prediction. Thirdly, on each kind of temporal scale, the results are subdivided into single-site prediction and regional prediction to sort out in detail. Finally, the results are analyzed on the basis of the predicted temporal scale, spatial scale and input data. It has been observed that most recent papers highlight the importance of short-term predictions. The machine learning method shows excellent nonlinear description ability in short-term prediction, the prediction results are satisfactory. The spatial average effect of regional prediction reduces the variability of solar energy, the prediction results are reliable. |
| first_indexed | 2024-12-21T10:56:59Z |
| format | Article |
| id | doaj.art-f21e1d73fa774a83ba0954afc2c390fe |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-21T10:56:59Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f21e1d73fa774a83ba0954afc2c390fe2022-12-21T19:06:29ZengIEEEIEEE Access2169-35362022-01-0110350733509310.1109/ACCESS.2022.31622069741707A Review of Multitemporal and Multispatial Scales Photovoltaic Forecasting MethodsCaicheng Liu0Ming Li1Yunjun Yu2https://orcid.org/0000-0001-5862-3102Ziyang Wu3Hai Gong4https://orcid.org/0000-0003-1675-5929Feier Cheng5College of Information Engineering, Nanchang University, Nanchang, ChinaCollege of Information Engineering, Nanchang University, Nanchang, ChinaCollege of Information Engineering, Nanchang University, Nanchang, ChinaSamueli School of Engineering, Irvine, CA, USACollege of Information Engineering, Nanchang University, Nanchang, ChinaCollege of Information Engineering, Nanchang University, Nanchang, ChinaReliable photovoltaic(PV) forecasting can provide important data support for power system operation, which is the key to realize the large-scale consumption of solar energy resources. PV forecasting task becomes crucial to ensure power system stability and economic operation. This paper reviews the existing research of PV forecasting methods from the perspective of multi-temporal scale and multi-spatial scale. Firstly, according to the forecasting process, demand, temporal and spatial scale, the forecasting methods are classified and the evaluation indicators involved in the research are listed. Secondly, based on the temporal scale of PV power generation, the results are combed through the three kind of scale of ultra-short-term, short-term and medium and long-term prediction. Thirdly, on each kind of temporal scale, the results are subdivided into single-site prediction and regional prediction to sort out in detail. Finally, the results are analyzed on the basis of the predicted temporal scale, spatial scale and input data. It has been observed that most recent papers highlight the importance of short-term predictions. The machine learning method shows excellent nonlinear description ability in short-term prediction, the prediction results are satisfactory. The spatial average effect of regional prediction reduces the variability of solar energy, the prediction results are reliable.https://ieeexplore.ieee.org/document/9741707/Solar energyphotovoltaic forecastingmachine learningmulti-temporal scalemulti-spatial scale |
| spellingShingle | Caicheng Liu Ming Li Yunjun Yu Ziyang Wu Hai Gong Feier Cheng A Review of Multitemporal and Multispatial Scales Photovoltaic Forecasting Methods IEEE Access Solar energy photovoltaic forecasting machine learning multi-temporal scale multi-spatial scale |
| title | A Review of Multitemporal and Multispatial Scales Photovoltaic Forecasting Methods |
| title_full | A Review of Multitemporal and Multispatial Scales Photovoltaic Forecasting Methods |
| title_fullStr | A Review of Multitemporal and Multispatial Scales Photovoltaic Forecasting Methods |
| title_full_unstemmed | A Review of Multitemporal and Multispatial Scales Photovoltaic Forecasting Methods |
| title_short | A Review of Multitemporal and Multispatial Scales Photovoltaic Forecasting Methods |
| title_sort | review of multitemporal and multispatial scales photovoltaic forecasting methods |
| topic | Solar energy photovoltaic forecasting machine learning multi-temporal scale multi-spatial scale |
| url | https://ieeexplore.ieee.org/document/9741707/ |
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