Solar photovoltaic power prediction using different machine learning methods
The main aim of the present study is to explore the relationship between numerous input parameters and the solar photovoltaic (PV) power using machine learning (ML) models. Two different ML approaches such as support vector machine (SVM) and Gaussian process regression (GPR) were considered and comp...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-04-01
|
| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484721013287 |
| _version_ | 1818022578642485248 |
|---|---|
| author | Bouchaib Zazoum |
| author_facet | Bouchaib Zazoum |
| author_sort | Bouchaib Zazoum |
| collection | DOAJ |
| description | The main aim of the present study is to explore the relationship between numerous input parameters and the solar photovoltaic (PV) power using machine learning (ML) models. Two different ML approaches such as support vector machine (SVM) and Gaussian process regression (GPR) were considered and compared. The basic input parameters including solar PV panel temperature, ambient temperature, solar flux, time of the day and relative humidity were considered for predicting the solar PV power. The results showed that among the proposed ML approaches, Matern 5/2 GPR algorithm provided the optimal performance; whereas cubic SVM had the worst performance. Furthermore, the predicted output results are in good agreement with the experimental values, indicating that the proposed ML approaches are appropriate for use in predicting the power of different solar PV panel. Additionally, to showcase the effectiveness and the accuracy of SVM and GPR models in forecasting solar PV power, the results of these models are compared using root mean squared error (RMSE) and mean absolute error (MAE) criteria. |
| first_indexed | 2024-12-10T03:30:39Z |
| format | Article |
| id | doaj.art-d24baf40c5bc423898ed2234b4239df8 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-12-10T03:30:39Z |
| publishDate | 2022-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-d24baf40c5bc423898ed2234b4239df82022-12-22T02:03:49ZengElsevierEnergy Reports2352-48472022-04-0181925Solar photovoltaic power prediction using different machine learning methodsBouchaib Zazoum0Department of Mechanical Engineering, Prince Mohammad Bin Fahd University, Al Khobar, Saudi ArabiaThe main aim of the present study is to explore the relationship between numerous input parameters and the solar photovoltaic (PV) power using machine learning (ML) models. Two different ML approaches such as support vector machine (SVM) and Gaussian process regression (GPR) were considered and compared. The basic input parameters including solar PV panel temperature, ambient temperature, solar flux, time of the day and relative humidity were considered for predicting the solar PV power. The results showed that among the proposed ML approaches, Matern 5/2 GPR algorithm provided the optimal performance; whereas cubic SVM had the worst performance. Furthermore, the predicted output results are in good agreement with the experimental values, indicating that the proposed ML approaches are appropriate for use in predicting the power of different solar PV panel. Additionally, to showcase the effectiveness and the accuracy of SVM and GPR models in forecasting solar PV power, the results of these models are compared using root mean squared error (RMSE) and mean absolute error (MAE) criteria.http://www.sciencedirect.com/science/article/pii/S2352484721013287Solar energyPV panelMachine learningSupport vector machine (SVM)Gaussian process regression (GPR) |
| spellingShingle | Bouchaib Zazoum Solar photovoltaic power prediction using different machine learning methods Energy Reports Solar energy PV panel Machine learning Support vector machine (SVM) Gaussian process regression (GPR) |
| title | Solar photovoltaic power prediction using different machine learning methods |
| title_full | Solar photovoltaic power prediction using different machine learning methods |
| title_fullStr | Solar photovoltaic power prediction using different machine learning methods |
| title_full_unstemmed | Solar photovoltaic power prediction using different machine learning methods |
| title_short | Solar photovoltaic power prediction using different machine learning methods |
| title_sort | solar photovoltaic power prediction using different machine learning methods |
| topic | Solar energy PV panel Machine learning Support vector machine (SVM) Gaussian process regression (GPR) |
| url | http://www.sciencedirect.com/science/article/pii/S2352484721013287 |
| work_keys_str_mv | AT bouchaibzazoum solarphotovoltaicpowerpredictionusingdifferentmachinelearningmethods |