Enhanced Ultra-Short-Term PV Forecasting Using Sky Imagers: Integrating MCR and Cloud Cover Estimation
Accurate photovoltaic (PV) power forecasting is crucial for stable grid integration, particularly under rapidly changing weather conditions. This study presents an ultra-short-term forecasting model that integrates sky imager data and meteorological radar data, achieving significant improvements in...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/1/28 |
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| _version_ | 1841549287913160704 |
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| author | Weixiong Wu Rui Gao Peng Wu Chen Yuan Xiaoling Xia Renfeng Liu Yifei Wang |
| author_facet | Weixiong Wu Rui Gao Peng Wu Chen Yuan Xiaoling Xia Renfeng Liu Yifei Wang |
| author_sort | Weixiong Wu |
| collection | DOAJ |
| description | Accurate photovoltaic (PV) power forecasting is crucial for stable grid integration, particularly under rapidly changing weather conditions. This study presents an ultra-short-term forecasting model that integrates sky imager data and meteorological radar data, achieving significant improvements in forecasting accuracy. By dynamically tracking cloud movement and estimating cloud coverage, the model enhances performance under both clear and cloudy conditions. Over an 8-day evaluation period, the average forecasting accuracy improved from 67.26% to 77.47% (+15%), with MSE reduced by 39.2% (from 481.5 to 292.6), <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> increased from 0.724 to 0.855, NSE improved from 0.725 to 0.851, and Theil’s U reduced from 0.42 to 0.32. Notable improvements were observed during abrupt weather transitions, particularly on 1 July and 3 July, where the combination of MCR and sky imager data demonstrated superior adaptability. This integrated approach provides a robust foundation for advancing ultra-short-term PV power forecasting. |
| format | Article |
| id | doaj-art-990412c6647749e5a272730dce7c8c6f |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-990412c6647749e5a272730dce7c8c6f2025-01-10T13:16:52ZengMDPI AGEnergies1996-10732024-12-011812810.3390/en18010028Enhanced Ultra-Short-Term PV Forecasting Using Sky Imagers: Integrating MCR and Cloud Cover EstimationWeixiong Wu0Rui Gao1Peng Wu2Chen Yuan3Xiaoling Xia4Renfeng Liu5Yifei Wang6Mamaya Photovoltaic Branch of Guizhou Beipanjiang Electric Power Co., Ltd., Guiyang 550081, ChinaMamaya Photovoltaic Branch of Guizhou Beipanjiang Electric Power Co., Ltd., Guiyang 550081, ChinaMamaya Photovoltaic Branch of Guizhou Beipanjiang Electric Power Co., Ltd., Guiyang 550081, ChinaGuizhou New Meteorological Technology Co., Ltd., Guiyang 550081, ChinaGuizhou New Meteorological Technology Co., Ltd., Guiyang 550081, ChinaSchool of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, ChinaSchool of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, ChinaAccurate photovoltaic (PV) power forecasting is crucial for stable grid integration, particularly under rapidly changing weather conditions. This study presents an ultra-short-term forecasting model that integrates sky imager data and meteorological radar data, achieving significant improvements in forecasting accuracy. By dynamically tracking cloud movement and estimating cloud coverage, the model enhances performance under both clear and cloudy conditions. Over an 8-day evaluation period, the average forecasting accuracy improved from 67.26% to 77.47% (+15%), with MSE reduced by 39.2% (from 481.5 to 292.6), <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> increased from 0.724 to 0.855, NSE improved from 0.725 to 0.851, and Theil’s U reduced from 0.42 to 0.32. Notable improvements were observed during abrupt weather transitions, particularly on 1 July and 3 July, where the combination of MCR and sky imager data demonstrated superior adaptability. This integrated approach provides a robust foundation for advancing ultra-short-term PV power forecasting.https://www.mdpi.com/1996-1073/18/1/28ultra-short-term PV power forecastingsky imagersMCRcloud cover estimation |
| spellingShingle | Weixiong Wu Rui Gao Peng Wu Chen Yuan Xiaoling Xia Renfeng Liu Yifei Wang Enhanced Ultra-Short-Term PV Forecasting Using Sky Imagers: Integrating MCR and Cloud Cover Estimation Energies ultra-short-term PV power forecasting sky imagers MCR cloud cover estimation |
| title | Enhanced Ultra-Short-Term PV Forecasting Using Sky Imagers: Integrating MCR and Cloud Cover Estimation |
| title_full | Enhanced Ultra-Short-Term PV Forecasting Using Sky Imagers: Integrating MCR and Cloud Cover Estimation |
| title_fullStr | Enhanced Ultra-Short-Term PV Forecasting Using Sky Imagers: Integrating MCR and Cloud Cover Estimation |
| title_full_unstemmed | Enhanced Ultra-Short-Term PV Forecasting Using Sky Imagers: Integrating MCR and Cloud Cover Estimation |
| title_short | Enhanced Ultra-Short-Term PV Forecasting Using Sky Imagers: Integrating MCR and Cloud Cover Estimation |
| title_sort | enhanced ultra short term pv forecasting using sky imagers integrating mcr and cloud cover estimation |
| topic | ultra-short-term PV power forecasting sky imagers MCR cloud cover estimation |
| url | https://www.mdpi.com/1996-1073/18/1/28 |
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