Forecasting Wind Energy Production Using Machine Learning Techniques
Wind energy is an essential source of renewable energy that has gained popularity in recent years. Accurately forecasting wind energy production is crucial for efficient energy management and distribution. This paper proposes a machine learning-based approach using Support Vector Regression (SVR) an...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/24/e3sconf_icseret2023_01007.pdf |
| _version_ | 1797808093389651968 |
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| author | Margarat G. Simi Kumar C. Siva Rajan Surulivel B. Raj Mohan |
| author_facet | Margarat G. Simi Kumar C. Siva Rajan Surulivel B. Raj Mohan |
| author_sort | Margarat G. Simi |
| collection | DOAJ |
| description | Wind energy is an essential source of renewable energy that has gained popularity in recent years. Accurately forecasting wind energy production is crucial for efficient energy management and distribution. This paper proposes a machine learning-based approach using Support Vector Regression (SVR) and Random Forest Regression (RFR) to forecast wind energy production. The proposed methodology involves data collection, preprocessing, feature selection, model training, optimization, and evaluation. The performance of the models is assessed using mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R-squared) metrics. The results indicate that the proposed SVR-RFR model outperforms individual models, achieving a higher accuracy in forecasting wind energy production. |
| first_indexed | 2024-03-13T06:32:23Z |
| format | Article |
| id | doaj.art-d8580dde041b4e969ce622896bc6ead1 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-13T06:32:23Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-d8580dde041b4e969ce622896bc6ead12023-06-09T09:06:53ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013870100710.1051/e3sconf/202338701007e3sconf_icseret2023_01007Forecasting Wind Energy Production Using Machine Learning TechniquesMargarat G. Simi0Kumar C. Siva1Rajan Surulivel2B. Raj Mohan3New Prince Shri Bhavani College Of Engineering and Technology, Approved by AICTE, Affiliated To Anna UniversityAssociate Professor, School of Computing, Mohan Babu Univesity (ERST While Sree Vidyanikethan Engineering College-Autonomous)Assistant Professor, School of Computing, Mohan Babu Univesity (ERST While Sree Vidyanikethan Engineering College-Autonomous)Prince Dr. K. Vasudevan College of Engineering and TechnologyAssistant Professor, Prince Shri Venkateshwara Padmavathy Engineering CollegeWind energy is an essential source of renewable energy that has gained popularity in recent years. Accurately forecasting wind energy production is crucial for efficient energy management and distribution. This paper proposes a machine learning-based approach using Support Vector Regression (SVR) and Random Forest Regression (RFR) to forecast wind energy production. The proposed methodology involves data collection, preprocessing, feature selection, model training, optimization, and evaluation. The performance of the models is assessed using mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R-squared) metrics. The results indicate that the proposed SVR-RFR model outperforms individual models, achieving a higher accuracy in forecasting wind energy production.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/24/e3sconf_icseret2023_01007.pdfwind energy productionmachine learningsupport vector regressionrandom forest regressionforecasting |
| spellingShingle | Margarat G. Simi Kumar C. Siva Rajan Surulivel B. Raj Mohan Forecasting Wind Energy Production Using Machine Learning Techniques E3S Web of Conferences wind energy production machine learning support vector regression random forest regression forecasting |
| title | Forecasting Wind Energy Production Using Machine Learning Techniques |
| title_full | Forecasting Wind Energy Production Using Machine Learning Techniques |
| title_fullStr | Forecasting Wind Energy Production Using Machine Learning Techniques |
| title_full_unstemmed | Forecasting Wind Energy Production Using Machine Learning Techniques |
| title_short | Forecasting Wind Energy Production Using Machine Learning Techniques |
| title_sort | forecasting wind energy production using machine learning techniques |
| topic | wind energy production machine learning support vector regression random forest regression forecasting |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/24/e3sconf_icseret2023_01007.pdf |
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