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...

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Bibliographic Details
Published inE3S Web of Conferences Vol. 387; p. 1007
Main Authors Margarat, G. Simi, Kumar C., Siva, Rajan, Surulivel, B., Raj Mohan
Format Journal Article Conference Proceeding
LanguageEnglish
Published Les Ulis EDP Sciences 01.01.2023
Subjects
Data collection
Energy distribution
Energy management
Forecasting
Learning algorithms
Machine learning
Mathematical models
Optimization
random forest regression
Renewable energy sources
Root-mean-square errors
Support vector machines
support vector regression
wind energy production
Wind power
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Summary: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.
ISSN:2267-1242
2555-0403
2267-1242
DOI:10.1051/e3sconf/202338701007