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Machine Learning Techniques for Short-Term Forecasting of Wind Power Generation

  • Yogesh GuptaEmail author
  • Amit Saraswat
Conference paper
  • 61 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1141)

Abstract

体育赛事投注记录in recent years, many countries have established their ambitious renewable energy targets to satisfy their future electricity demand with the main aim to foster sustainable and low-emission development. in meeting these targets, the changes to power system planning and operations involve the significant consideration of renewable energy generation through mainly wind energy and solar energy which are more variable and uncertain as compared to the conventional sources (i.e. thermal and nuclear energy). in the present paper, three machine learning methods named as support vector machine, artificial neural network and multiple linear regression are applied to forecast wind power generation on basis of past data of wind direction and wind speed. the impact of input variables such as wind speed and wind direction on wind power generation is investigated and compared.

Keywords

Wind power Artificial neural network Support vector machine Regression Multilayer perceptron 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.Depatment of Computer Science and Engineering, Manipal University JaipurJaipurIndia
  2. 2.Depatment of Electrical Engineering, Manipal University JaipurJaipurIndia

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