Assessing the performance of physical transposition models in photovoltaic power forecasting: A comprehensive micro and macro accuracy analysis
Accurate power prediction is crucial for the design, simulation, and performance assessment of Photovoltaic (PV) systems. Achieving this accuracy relies significantly on the precise plane-of-array (POA) irradiance data, which is seldom directly measured. To mitigate this limitation, transposition mo...
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Elsevier
2024-10-01
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| Series: | Energy Conversion and Management: X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590174524002708 |
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| author | Eslam Mahmoudi João Lucas de Souza Silva Tárcio André dos Santos Barros |
| author_facet | Eslam Mahmoudi João Lucas de Souza Silva Tárcio André dos Santos Barros |
| author_sort | Eslam Mahmoudi |
| collection | DOAJ |
| description | Accurate power prediction is crucial for the design, simulation, and performance assessment of Photovoltaic (PV) systems. Achieving this accuracy relies significantly on the precise plane-of-array (POA) irradiance data, which is seldom directly measured. To mitigate this limitation, transposition models have been developed to estimate POA irradiance. Numerous studies have evaluated the performance of these models using horizontal irradiances and POA measurements from specific locations. However, the absence of POA measurements poses a significant challenge to using the proposed methods in transposition model evaluation. Moreover, these studies have overlooked the critical role of POA data in predicting PV power.This study addresses these challenges by assessing the performance of thirty two transposition models within a detailed PV power forecasting framework. A comprehensive micro- and macro-level accuracy evaluation is conducted using predicted and ground-measured energy data from three distinct PV systems in different countries. The evaluation is carried out across hourly, daily, monthly, and annual time scales using four distinct statistical metrics under all-sky and the four sky-conditions.The simulation results validate the effectiveness of the proposed approach in assessing the transposition models in the absence of POA irradiance data. Consistent with previous studies, the findings reaffirm the dependence of transposition model performance on location, climate, time, and cloud cover. Furthermore, the statistical analyses identify the best-performing transposition models for each case study under all-sky and sky-conditions scenarios, providing practical guidelines for model selection. |
| format | Article |
| id | doaj-art-a9f2fa6eb2f4488b8e06e1d3652b28f6 |
| institution | Kabale University |
| issn | 2590-1745 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Conversion and Management: X |
| spelling | doaj-art-a9f2fa6eb2f4488b8e06e1d3652b28f62024-12-18T08:51:40ZengElsevierEnergy Conversion and Management: X2590-17452024-10-0124100792Assessing the performance of physical transposition models in photovoltaic power forecasting: A comprehensive micro and macro accuracy analysisEslam Mahmoudi0João Lucas de Souza Silva1Tárcio André dos Santos Barros2Corresponding author.; Department of System and Energy, Faculty of Electrical and Computer Engineering, Universidade Estadual de Campinas, Av. Albert Einstein, N∘400 - Cidade Universitária, Campinas, 13083-852, SP, BrazilDepartment of System and Energy, Faculty of Electrical and Computer Engineering, Universidade Estadual de Campinas, Av. Albert Einstein, N∘400 - Cidade Universitária, Campinas, 13083-852, SP, BrazilDepartment of System and Energy, Faculty of Electrical and Computer Engineering, Universidade Estadual de Campinas, Av. Albert Einstein, N∘400 - Cidade Universitária, Campinas, 13083-852, SP, BrazilAccurate power prediction is crucial for the design, simulation, and performance assessment of Photovoltaic (PV) systems. Achieving this accuracy relies significantly on the precise plane-of-array (POA) irradiance data, which is seldom directly measured. To mitigate this limitation, transposition models have been developed to estimate POA irradiance. Numerous studies have evaluated the performance of these models using horizontal irradiances and POA measurements from specific locations. However, the absence of POA measurements poses a significant challenge to using the proposed methods in transposition model evaluation. Moreover, these studies have overlooked the critical role of POA data in predicting PV power.This study addresses these challenges by assessing the performance of thirty two transposition models within a detailed PV power forecasting framework. A comprehensive micro- and macro-level accuracy evaluation is conducted using predicted and ground-measured energy data from three distinct PV systems in different countries. The evaluation is carried out across hourly, daily, monthly, and annual time scales using four distinct statistical metrics under all-sky and the four sky-conditions.The simulation results validate the effectiveness of the proposed approach in assessing the transposition models in the absence of POA irradiance data. Consistent with previous studies, the findings reaffirm the dependence of transposition model performance on location, climate, time, and cloud cover. Furthermore, the statistical analyses identify the best-performing transposition models for each case study under all-sky and sky-conditions scenarios, providing practical guidelines for model selection.http://www.sciencedirect.com/science/article/pii/S2590174524002708Physical transposition modelsPV power forecastingMicro- and macro-level accuracy evaluationAll-skySky-conditions |
| spellingShingle | Eslam Mahmoudi João Lucas de Souza Silva Tárcio André dos Santos Barros Assessing the performance of physical transposition models in photovoltaic power forecasting: A comprehensive micro and macro accuracy analysis Energy Conversion and Management: X Physical transposition models PV power forecasting Micro- and macro-level accuracy evaluation All-sky Sky-conditions |
| title | Assessing the performance of physical transposition models in photovoltaic power forecasting: A comprehensive micro and macro accuracy analysis |
| title_full | Assessing the performance of physical transposition models in photovoltaic power forecasting: A comprehensive micro and macro accuracy analysis |
| title_fullStr | Assessing the performance of physical transposition models in photovoltaic power forecasting: A comprehensive micro and macro accuracy analysis |
| title_full_unstemmed | Assessing the performance of physical transposition models in photovoltaic power forecasting: A comprehensive micro and macro accuracy analysis |
| title_short | Assessing the performance of physical transposition models in photovoltaic power forecasting: A comprehensive micro and macro accuracy analysis |
| title_sort | assessing the performance of physical transposition models in photovoltaic power forecasting a comprehensive micro and macro accuracy analysis |
| topic | Physical transposition models PV power forecasting Micro- and macro-level accuracy evaluation All-sky Sky-conditions |
| url | http://www.sciencedirect.com/science/article/pii/S2590174524002708 |
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