Prediction of Photovoltaic Module Characteristics by Machine Learning for Renewable Energy Applications
Artykuł w czasopiśmie
MNiSW
100
Lista 2024
| Status: | |
| Autorzy: | Porowski Rafał, Kowalik Robert, Szeląg Bartosz, Komendołowicz Diana, Białek Anita, Janaszek Agata, Piłat-Rożek Magdalena, Łazuka Ewa, Gorzelnik Tomasz |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2025 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 16 |
| Wolumen/Tom: | 15 |
| Numer artykułu: | 8868 |
| Strony: | 1 - 19 |
| Impact Factor: | 2,5 |
| Web of Science® Times Cited: | 0 |
| Scopus® Cytowania: | 0 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 30 lipca 2025 |
| Abstrakty: | angielski |
| Photovoltaic (PV) modules undergo comprehensive testing to validate their electrical and thermal properties prior to market entry. These evaluations consist of durability and effi- ciency tests performed under realistic outdoor conditions with natural climatic influences, as well as in controlled laboratory settings. The overall performance of PV cells is affected by several factors, including solar irradiance, operating temperature, installation site pa- rameters, prevailing weather, and shading effects. In the presented study, three distinct PV modules were analyzed using a sophisticated large-scale steady-state solar simulator. The current–voltage (I-V) characteristics of each module were precisely measured and subsequently scrutinized. To augment the analysis, a three-layer artificial neural network, specifically the multilayer perceptron (MLP), was developed. The experimental measure- ments, along with the outputs derived from the MLP model, served as the foundation for a comprehensive global sensitivity analysis (GSA). The experimental results revealed variances between the manufacturer’s declared values and those recorded during testing. The first module achieved a maximum power point that exceeded the manufacturer’s spec- ification. Conversely, the second and third modules delivered power values corresponding to only 85–87% and 95–98% of their stated capacities, respectively. The global sensitivity analysis further indicated that while certain parameters, such as efficiency and the ratio of Voc/V, played a dominant role in influencing the power-voltage relationship, another parameter, U, exhibited a comparatively minor effect. These results highlight the significant potential of integrating machine learning techniques into the performance evaluation and predictive analysis of photovoltaic modules |
