An investigation of photovoltaic power forecasting in buildings considering shadow effects: Modeling approach and SHAP analysis
The power generation of distributed photovoltaic (PV) systems often suffers interference due to shadows cast by surrounding buildings. To improve the accuracy of PV power forecasts, this paper presents a PV power prediction method that takes shadow effects into consideration. Firstly, a convenient P...
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| Published in: | Renewable energy Vol. 245; p. 122821 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.06.2025
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| ISSN: | 0960-1481 |
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| Abstract | The power generation of distributed photovoltaic (PV) systems often suffers interference due to shadows cast by surrounding buildings. To improve the accuracy of PV power forecasts, this paper presents a PV power prediction method that takes shadow effects into consideration. Firstly, a convenient PV shadow model was formulated for predicting the proportion of PV shaded (PPS), using theoretical derivation and a zoning shading judgment strategy. Subsequently, a PV power prediction method was proposed based on PV shadow forecasting and the convolutional deep neural network algorithm. Finally, this method was applied to a carport PV system in a building in Beijing, China, and SHAP analysis was utilized for the interpretation. The results show that the proposed method can automatically recognize shadow conditions, and significantly improve the predictive accuracy of PV power, reducing the MAE by 10.1 % and increasing the R2 value from 0.91 to 0.94. The ranking of feature importance to the PV power prediction model is as follows: solar radiation, hour, ambient temperature, PPS, and relative humidity. This study offers a feasible solution for predicting power generation of PV systems that are subject to shadow shading from buildings. |
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| AbstractList | The power generation of distributed photovoltaic (PV) systems often suffers interference due to shadows cast by surrounding buildings. To improve the accuracy of PV power forecasts, this paper presents a PV power prediction method that takes shadow effects into consideration. Firstly, a convenient PV shadow model was formulated for predicting the proportion of PV shaded (PPS), using theoretical derivation and a zoning shading judgment strategy. Subsequently, a PV power prediction method was proposed based on PV shadow forecasting and the convolutional deep neural network algorithm. Finally, this method was applied to a carport PV system in a building in Beijing, China, and SHAP analysis was utilized for the interpretation. The results show that the proposed method can automatically recognize shadow conditions, and significantly improve the predictive accuracy of PV power, reducing the MAE by 10.1 % and increasing the R2 value from 0.91 to 0.94. The ranking of feature importance to the PV power prediction model is as follows: solar radiation, hour, ambient temperature, PPS, and relative humidity. This study offers a feasible solution for predicting power generation of PV systems that are subject to shadow shading from buildings. The power generation of distributed photovoltaic (PV) systems often suffers interference due to shadows cast by surrounding buildings. To improve the accuracy of PV power forecasts, this paper presents a PV power prediction method that takes shadow effects into consideration. Firstly, a convenient PV shadow model was formulated for predicting the proportion of PV shaded (PPS), using theoretical derivation and a zoning shading judgment strategy. Subsequently, a PV power prediction method was proposed based on PV shadow forecasting and the convolutional deep neural network algorithm. Finally, this method was applied to a carport PV system in a building in Beijing, China, and SHAP analysis was utilized for the interpretation. The results show that the proposed method can automatically recognize shadow conditions, and significantly improve the predictive accuracy of PV power, reducing the MAE by 10.1 % and increasing the R² value from 0.91 to 0.94. The ranking of feature importance to the PV power prediction model is as follows: solar radiation, hour, ambient temperature, PPS, and relative humidity. This study offers a feasible solution for predicting power generation of PV systems that are subject to shadow shading from buildings. |
| ArticleNumber | 122821 |
| Author | Wei, Wenzhe Wang, Wei Di, Haoran Han, Shulun Sun, Yuying Li, Yunhe Ren, Jinyang Fu, Jiaqian |
| Author_xml | – sequence: 1 givenname: Jiaqian surname: Fu fullname: Fu, Jiaqian organization: Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China – sequence: 2 givenname: Yuying surname: Sun fullname: Sun, Yuying organization: Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China – sequence: 3 givenname: Yunhe surname: Li fullname: Li, Yunhe organization: Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China – sequence: 4 givenname: Wei orcidid: 0000-0003-3680-7191 surname: Wang fullname: Wang, Wei email: mrwangwei@bjut.edu.cn organization: Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China – sequence: 5 givenname: Wenzhe surname: Wei fullname: Wei, Wenzhe organization: Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China – sequence: 6 givenname: Jinyang surname: Ren fullname: Ren, Jinyang organization: Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China – sequence: 7 givenname: Shulun surname: Han fullname: Han, Shulun organization: Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China – sequence: 8 givenname: Haoran surname: Di fullname: Di, Haoran organization: Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China |
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| Keywords | Power prediction SHAP analysis Photovoltaic power Shadow effect Convolutional deep neural networks |
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| SubjectTerms | algorithms ambient temperature China Convolutional deep neural networks Photovoltaic power power generation Power prediction prediction relative humidity Shadow effect SHAP analysis solar collectors solar energy solar radiation |
| Title | An investigation of photovoltaic power forecasting in buildings considering shadow effects: Modeling approach and SHAP analysis |
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