Regression Model to Predict Global Solar Irradiance in Malaysia
A novel regression model is developed to estimate the monthly global solar irradiance in Malaysia. The model is developed based on different available meteorological parameters, including temperature, cloud cover, rain precipitate, relative humidity, wind speed, pressure, and gust speed, by implemen...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2015/347023 |
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| _version_ | 1832548403338280960 |
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| author | Hairuniza Ahmed Kutty Muhammad Hazim Masral Parvathy Rajendran |
| author_facet | Hairuniza Ahmed Kutty Muhammad Hazim Masral Parvathy Rajendran |
| author_sort | Hairuniza Ahmed Kutty |
| collection | DOAJ |
| description | A novel regression model is developed to estimate the monthly global solar irradiance in Malaysia. The model is developed based on different available meteorological parameters, including temperature, cloud cover, rain precipitate, relative humidity, wind speed, pressure, and gust speed, by implementing regression analysis. This paper reports on the details of the analysis of the effect of each prediction parameter to identify the parameters that are relevant to estimating global solar irradiance. In addition, the proposed model is compared in terms of the root mean square error (RMSE), mean bias error (MBE), and the coefficient of determination (R2) with other models available from literature studies. Seven models based on single parameters (PM1 to PM7) and five multiple-parameter models (PM7 to PM12) are proposed. The new models perform well, with RMSE ranging from 0.429% to 1.774%, R2 ranging from 0.942 to 0.992, and MBE ranging from −0.1571% to 0.6025%. In general, cloud cover significantly affects the estimation of global solar irradiance. However, cloud cover in Malaysia lacks sufficient influence when included into multiple-parameter models although it performs fairly well in single-parameter prediction models. |
| format | Article |
| id | doaj-art-7c7f2ffa32654a1084fd8cd9ef69278e |
| institution | Kabale University |
| issn | 1110-662X 1687-529X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-7c7f2ffa32654a1084fd8cd9ef69278e2025-02-03T06:14:06ZengWileyInternational Journal of Photoenergy1110-662X1687-529X2015-01-01201510.1155/2015/347023347023Regression Model to Predict Global Solar Irradiance in MalaysiaHairuniza Ahmed Kutty0Muhammad Hazim Masral1Parvathy Rajendran2School of Aerospace Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, MalaysiaSchool of Aerospace Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, MalaysiaSchool of Aerospace Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, MalaysiaA novel regression model is developed to estimate the monthly global solar irradiance in Malaysia. The model is developed based on different available meteorological parameters, including temperature, cloud cover, rain precipitate, relative humidity, wind speed, pressure, and gust speed, by implementing regression analysis. This paper reports on the details of the analysis of the effect of each prediction parameter to identify the parameters that are relevant to estimating global solar irradiance. In addition, the proposed model is compared in terms of the root mean square error (RMSE), mean bias error (MBE), and the coefficient of determination (R2) with other models available from literature studies. Seven models based on single parameters (PM1 to PM7) and five multiple-parameter models (PM7 to PM12) are proposed. The new models perform well, with RMSE ranging from 0.429% to 1.774%, R2 ranging from 0.942 to 0.992, and MBE ranging from −0.1571% to 0.6025%. In general, cloud cover significantly affects the estimation of global solar irradiance. However, cloud cover in Malaysia lacks sufficient influence when included into multiple-parameter models although it performs fairly well in single-parameter prediction models.http://dx.doi.org/10.1155/2015/347023 |
| spellingShingle | Hairuniza Ahmed Kutty Muhammad Hazim Masral Parvathy Rajendran Regression Model to Predict Global Solar Irradiance in Malaysia International Journal of Photoenergy |
| title | Regression Model to Predict Global Solar Irradiance in Malaysia |
| title_full | Regression Model to Predict Global Solar Irradiance in Malaysia |
| title_fullStr | Regression Model to Predict Global Solar Irradiance in Malaysia |
| title_full_unstemmed | Regression Model to Predict Global Solar Irradiance in Malaysia |
| title_short | Regression Model to Predict Global Solar Irradiance in Malaysia |
| title_sort | regression model to predict global solar irradiance in malaysia |
| url | http://dx.doi.org/10.1155/2015/347023 |
| work_keys_str_mv | AT hairunizaahmedkutty regressionmodeltopredictglobalsolarirradianceinmalaysia AT muhammadhazimmasral regressionmodeltopredictglobalsolarirradianceinmalaysia AT parvathyrajendran regressionmodeltopredictglobalsolarirradianceinmalaysia |