Data-Driven Hyperparameter Optimized Extreme Gradient Boosting Machine Learning Model for Solar Radiation Forecasting
The uncertainty of the non-conventional sources especially solar energy caused due to spatio-temporal factors like temperature, pressure, relative humidity etc. is continuously disrupting the productivity and reliability of an integrated power system which motivates the researcher or energy industry...
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| Format: | Article |
| Language: | English |
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VSB-Technical University of Ostrava
2022-01-01
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| Series: | Advances in Electrical and Electronic Engineering |
| Subjects: | |
| Online Access: | http://advances.utc.sk/index.php/AEEE/article/view/4650 |
| _version_ | 1827947476141735936 |
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| author | Kumari Namrata Mantosh Kumar Nishant Kumar |
| author_facet | Kumari Namrata Mantosh Kumar Nishant Kumar |
| author_sort | Kumari Namrata |
| collection | DOAJ |
| description | The uncertainty of the non-conventional sources especially solar energy caused due to spatio-temporal factors like temperature, pressure, relative humidity etc. is continuously disrupting the productivity and reliability of an integrated power system which motivates the researcher or energy industry for strategic forecasting solutions to enhance the proper scheduling and control of solar generation power plants. Several studies have been carried out; but still the objective of achieving accurate forecasting dependent on the spatio-temporal features is not achieved. To address this critical forecasting issue in this research article a hyper parametric tuning of the Extreme Gradient Boosting (XGB) machine learning model has been carried out using two met heuristic algorithms: Moth Flame Optimization (MFO) and Grey Wolf Optimization (GWO). The dataset comprises five years of metrological attributes collected from the National Renewable Energy Laboratory (NREL) for analysis. The validation of the proposed model has been done based on the five statistical errors: Max Error (ME), Mean Absolute Error (MAE), Coefficient of Determination (R^2), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The regressive assessment of all three models has confirmed that the XGB-MFO model outperformed the others as showing the highest R^2 score of 0.9337, 0.9011, 0.8744 and lowest RMSE values of 76.29 Wcm^{-2}, 41.90 Wm^{-2} and 95.94Wm^{-2} for Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (DHI) and Direct Normal Irradiance (DNI) respectively which ensures the proposed model implementation for the prediction and production of solar power. |
| first_indexed | 2024-04-09T12:39:32Z |
| format | Article |
| id | doaj.art-3a7ad7cc2113414f846aec1edff75834 |
| institution | Directory Open Access Journal |
| issn | 1336-1376 1804-3119 |
| language | English |
| last_indexed | 2024-04-09T12:39:32Z |
| publishDate | 2022-01-01 |
| publisher | VSB-Technical University of Ostrava |
| record_format | Article |
| series | Advances in Electrical and Electronic Engineering |
| spelling | doaj.art-3a7ad7cc2113414f846aec1edff758342023-05-14T20:50:14ZengVSB-Technical University of OstravaAdvances in Electrical and Electronic Engineering1336-13761804-31192022-01-0120454955910.15598/aeee.v20i4.46501184Data-Driven Hyperparameter Optimized Extreme Gradient Boosting Machine Learning Model for Solar Radiation ForecastingKumari Namrata0Mantosh Kumar1Nishant Kumar2Department of Electrical Engineering, National Institute of Technology, Adityapur, 831014 Jamshedpur, Jharkhand, IndiaDepartment of Electrical Engineering, National Institute of Technology, Adityapur, 831014 Jamshedpur, Jharkhand, IndiaDepartment of Electrical Engineering, B. K. Birla Institute of Engineering & Technology, BKBIET Campus, CEERI Road, 333031 Pilani, Rajasthan, IndiaThe uncertainty of the non-conventional sources especially solar energy caused due to spatio-temporal factors like temperature, pressure, relative humidity etc. is continuously disrupting the productivity and reliability of an integrated power system which motivates the researcher or energy industry for strategic forecasting solutions to enhance the proper scheduling and control of solar generation power plants. Several studies have been carried out; but still the objective of achieving accurate forecasting dependent on the spatio-temporal features is not achieved. To address this critical forecasting issue in this research article a hyper parametric tuning of the Extreme Gradient Boosting (XGB) machine learning model has been carried out using two met heuristic algorithms: Moth Flame Optimization (MFO) and Grey Wolf Optimization (GWO). The dataset comprises five years of metrological attributes collected from the National Renewable Energy Laboratory (NREL) for analysis. The validation of the proposed model has been done based on the five statistical errors: Max Error (ME), Mean Absolute Error (MAE), Coefficient of Determination (R^2), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The regressive assessment of all three models has confirmed that the XGB-MFO model outperformed the others as showing the highest R^2 score of 0.9337, 0.9011, 0.8744 and lowest RMSE values of 76.29 Wcm^{-2}, 41.90 Wm^{-2} and 95.94Wm^{-2} for Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (DHI) and Direct Normal Irradiance (DNI) respectively which ensures the proposed model implementation for the prediction and production of solar power.http://advances.utc.sk/index.php/AEEE/article/view/4650extreme gradient boostingforecastinggrey wolf optimizationmoth flame optimizationsolar irradiance. |
| spellingShingle | Kumari Namrata Mantosh Kumar Nishant Kumar Data-Driven Hyperparameter Optimized Extreme Gradient Boosting Machine Learning Model for Solar Radiation Forecasting Advances in Electrical and Electronic Engineering extreme gradient boosting forecasting grey wolf optimization moth flame optimization solar irradiance. |
| title | Data-Driven Hyperparameter Optimized Extreme Gradient Boosting Machine Learning Model for Solar Radiation Forecasting |
| title_full | Data-Driven Hyperparameter Optimized Extreme Gradient Boosting Machine Learning Model for Solar Radiation Forecasting |
| title_fullStr | Data-Driven Hyperparameter Optimized Extreme Gradient Boosting Machine Learning Model for Solar Radiation Forecasting |
| title_full_unstemmed | Data-Driven Hyperparameter Optimized Extreme Gradient Boosting Machine Learning Model for Solar Radiation Forecasting |
| title_short | Data-Driven Hyperparameter Optimized Extreme Gradient Boosting Machine Learning Model for Solar Radiation Forecasting |
| title_sort | data driven hyperparameter optimized extreme gradient boosting machine learning model for solar radiation forecasting |
| topic | extreme gradient boosting forecasting grey wolf optimization moth flame optimization solar irradiance. |
| url | http://advances.utc.sk/index.php/AEEE/article/view/4650 |
| work_keys_str_mv | AT kumarinamrata datadrivenhyperparameteroptimizedextremegradientboostingmachinelearningmodelforsolarradiationforecasting AT mantoshkumar datadrivenhyperparameteroptimizedextremegradientboostingmachinelearningmodelforsolarradiationforecasting AT nishantkumar datadrivenhyperparameteroptimizedextremegradientboostingmachinelearningmodelforsolarradiationforecasting |