Short-Term Wind Speed Forecasting Using Ensemble Learning

Wind speed forecasting plays a vital role in reliable operation and future planning ofwind turbines in smart grid to meet growing power demand. This article presents ensemble learning based short-term wind speed forecasting. Regression models developed using boosted trees and bagged trees in ensembl...

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Bibliographic Details
Published in2021 7th International Conference on Electrical Energy Systems (ICEES) pp. 502 - 506
Main Authors Karthikeyan, M., Rengaraj, R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 11.02.2021
Subjects
bagged trees
boosted trees
ensemble learning
Forecasting
Predictive models
Real-time systems
Regression tree analysis
Short-term wind speed forecasting
support vector regression
Turbines
Wind forecasting
Wind speed
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Summary:Wind speed forecasting plays a vital role in reliable operation and future planning ofwind turbines in smart grid to meet growing power demand. This article presents ensemble learning based short-term wind speed forecasting. Regression models developed using boosted trees and bagged trees in ensemble learning are used to predict the wind speed. The regression models are trained and tested with historical dataset of Rameswaram located in India. Compared with support vector regression (SVR), ensemble-based wind speed forecasting performs short-term wind speed forecasting effectively.
DOI:10.1109/ICEES51510.2021.9383718