Forecasting Solar Energy Production Using Machine Learning
When it comes to large-scale renewable energy plants, the future of solar power forecasting is vital to their success. For reliable predictions of solar electricity generation, one must take into consideration changes in weather patterns over time. In this paper, a hybrid model that integrates machi...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2022/7797488 |
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| _version_ | 1849307283590217728 |
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| author | C. Vennila Anita Titus T. Sri Sudha U. Sreenivasulu N. Pandu Ranga Reddy K. Jamal Dayadi Lakshmaiah P. Jagadeesh Assefa Belay |
| author_facet | C. Vennila Anita Titus T. Sri Sudha U. Sreenivasulu N. Pandu Ranga Reddy K. Jamal Dayadi Lakshmaiah P. Jagadeesh Assefa Belay |
| author_sort | C. Vennila |
| collection | DOAJ |
| description | When it comes to large-scale renewable energy plants, the future of solar power forecasting is vital to their success. For reliable predictions of solar electricity generation, one must take into consideration changes in weather patterns over time. In this paper, a hybrid model that integrates machine learning and statistical approaches is suggested for predicting future solar energy generation. In order to improve the accuracy of the suggested model, an ensemble of machine learning models was used in this study. The results of the simulation show that the proposed method has reduced placement cost, when compared with existing methods. When comparing the performance of an ensemble model that integrates all of the combination strategies to standard individual models, the suggested ensemble model outperformed the conventional individual models. According to the findings, a hybrid model that made use of both machine learning and statistics outperformed a model that made sole use of machine learning in its performance. |
| format | Article |
| id | doaj-art-22ad78d22e144ee6907ce4da9079bc81 |
| institution | Kabale University |
| issn | 1687-529X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-22ad78d22e144ee6907ce4da9079bc812025-08-20T03:54:48ZengWileyInternational Journal of Photoenergy1687-529X2022-01-01202210.1155/2022/7797488Forecasting Solar Energy Production Using Machine LearningC. Vennila0Anita Titus1T. Sri Sudha2U. Sreenivasulu3N. Pandu Ranga Reddy4K. Jamal5Dayadi Lakshmaiah6P. Jagadeesh7Assefa Belay8Department of Electrical and Electronics EngineeringDepartment of Electronics and Communication EngineeringDepartment of Electronics and Communication EngineeringDepartment of Electronics and Communication EngineeringDepartment of Electronics and Communication EngineeringDepartment of Electronics and Communication EngineeringDepartment of Electronics and Communication EngineeringDepartment of Electronics and Communication EngineeringDepartment of Mechanical EngineeringWhen it comes to large-scale renewable energy plants, the future of solar power forecasting is vital to their success. For reliable predictions of solar electricity generation, one must take into consideration changes in weather patterns over time. In this paper, a hybrid model that integrates machine learning and statistical approaches is suggested for predicting future solar energy generation. In order to improve the accuracy of the suggested model, an ensemble of machine learning models was used in this study. The results of the simulation show that the proposed method has reduced placement cost, when compared with existing methods. When comparing the performance of an ensemble model that integrates all of the combination strategies to standard individual models, the suggested ensemble model outperformed the conventional individual models. According to the findings, a hybrid model that made use of both machine learning and statistics outperformed a model that made sole use of machine learning in its performance.http://dx.doi.org/10.1155/2022/7797488 |
| spellingShingle | C. Vennila Anita Titus T. Sri Sudha U. Sreenivasulu N. Pandu Ranga Reddy K. Jamal Dayadi Lakshmaiah P. Jagadeesh Assefa Belay Forecasting Solar Energy Production Using Machine Learning International Journal of Photoenergy |
| title | Forecasting Solar Energy Production Using Machine Learning |
| title_full | Forecasting Solar Energy Production Using Machine Learning |
| title_fullStr | Forecasting Solar Energy Production Using Machine Learning |
| title_full_unstemmed | Forecasting Solar Energy Production Using Machine Learning |
| title_short | Forecasting Solar Energy Production Using Machine Learning |
| title_sort | forecasting solar energy production using machine learning |
| url | http://dx.doi.org/10.1155/2022/7797488 |
| work_keys_str_mv | AT cvennila forecastingsolarenergyproductionusingmachinelearning AT anitatitus forecastingsolarenergyproductionusingmachinelearning AT tsrisudha forecastingsolarenergyproductionusingmachinelearning AT usreenivasulu forecastingsolarenergyproductionusingmachinelearning AT npandurangareddy forecastingsolarenergyproductionusingmachinelearning AT kjamal forecastingsolarenergyproductionusingmachinelearning AT dayadilakshmaiah forecastingsolarenergyproductionusingmachinelearning AT pjagadeesh forecastingsolarenergyproductionusingmachinelearning AT assefabelay forecastingsolarenergyproductionusingmachinelearning |