Mapping Prediction of Surface Solar Radiation with Linear Regression Models: Case Study over Reunion Island
This paper presents a novel mapping prediction method for surface solar radiation with linear regression models. The dataset for surface solar radiation prediction is the daily surface incoming shortwave radiation (SIS) product from CM SAF SARAH-E. The spatial resolution is 0.05° × 0.05° and the tem...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/14/9/1331 |
| _version_ | 1797581373686415360 |
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| author | Qi Li Miloud Bessafi Peng Li |
| author_facet | Qi Li Miloud Bessafi Peng Li |
| author_sort | Qi Li |
| collection | DOAJ |
| description | This paper presents a novel mapping prediction method for surface solar radiation with linear regression models. The dataset for surface solar radiation prediction is the daily surface incoming shortwave radiation (SIS) product from CM SAF SARAH-E. The spatial resolution is 0.05° × 0.05° and the temporal coverage is from 2007 to 2016. The first five years (2007–2011) are used as training data, and the remaining five years (2012–2016) are used as test data in the prediction model. Datasets were detrended, de-seasonalized, and normalized before being applied to multiple linear regression (MLR), principal component regression (PCR), stepwise regression (SR), and partial least squares regression (PLSR), which are used to perform prediction mapping. The statistical analysis using MAE, MSE, and RMSE shows that the PCR model had the smallest MAE, MSE, and RMSE as compared to the other three models. The PCR model seems better for SSR mapping prediction over Reunion Island. Although the PCR model provides better prediction results, its MAE, MSE, and RMSE are quite large. |
| first_indexed | 2024-03-10T23:03:34Z |
| format | Article |
| id | doaj.art-6578993dff7247f39fa3e5ec08ec7d57 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T23:03:34Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-6578993dff7247f39fa3e5ec08ec7d572023-11-19T09:29:49ZengMDPI AGAtmosphere2073-44332023-08-01149133110.3390/atmos14091331Mapping Prediction of Surface Solar Radiation with Linear Regression Models: Case Study over Reunion IslandQi Li0Miloud Bessafi1Peng Li2Guangxi University of Science and Technology, 2 Avenue Wenchang, Chengzhong District, Liuzhou 545006, ChinaENERGY-Lab, University of La Réunion, 15, Avenue René Cassin CS 92003, CEDEX 9, 97744 Saint-Denis, FranceGuangxi University of Science and Technology, 2 Avenue Wenchang, Chengzhong District, Liuzhou 545006, ChinaThis paper presents a novel mapping prediction method for surface solar radiation with linear regression models. The dataset for surface solar radiation prediction is the daily surface incoming shortwave radiation (SIS) product from CM SAF SARAH-E. The spatial resolution is 0.05° × 0.05° and the temporal coverage is from 2007 to 2016. The first five years (2007–2011) are used as training data, and the remaining five years (2012–2016) are used as test data in the prediction model. Datasets were detrended, de-seasonalized, and normalized before being applied to multiple linear regression (MLR), principal component regression (PCR), stepwise regression (SR), and partial least squares regression (PLSR), which are used to perform prediction mapping. The statistical analysis using MAE, MSE, and RMSE shows that the PCR model had the smallest MAE, MSE, and RMSE as compared to the other three models. The PCR model seems better for SSR mapping prediction over Reunion Island. Although the PCR model provides better prediction results, its MAE, MSE, and RMSE are quite large.https://www.mdpi.com/2073-4433/14/9/1331surface solar radiationlinear regression modelprincipal component analysismapping prediction |
| spellingShingle | Qi Li Miloud Bessafi Peng Li Mapping Prediction of Surface Solar Radiation with Linear Regression Models: Case Study over Reunion Island Atmosphere surface solar radiation linear regression model principal component analysis mapping prediction |
| title | Mapping Prediction of Surface Solar Radiation with Linear Regression Models: Case Study over Reunion Island |
| title_full | Mapping Prediction of Surface Solar Radiation with Linear Regression Models: Case Study over Reunion Island |
| title_fullStr | Mapping Prediction of Surface Solar Radiation with Linear Regression Models: Case Study over Reunion Island |
| title_full_unstemmed | Mapping Prediction of Surface Solar Radiation with Linear Regression Models: Case Study over Reunion Island |
| title_short | Mapping Prediction of Surface Solar Radiation with Linear Regression Models: Case Study over Reunion Island |
| title_sort | mapping prediction of surface solar radiation with linear regression models case study over reunion island |
| topic | surface solar radiation linear regression model principal component analysis mapping prediction |
| url | https://www.mdpi.com/2073-4433/14/9/1331 |
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