Support vector regression-bald eagle search optimizer-based hybrid approach for short-term wind power forecasting

نویسندگان

چکیده

Abstract Wind power forecasting deals with the prediction of expected generation wind farms in next few minutes, hours, or days. The application machine learning techniques has become great interest due to their superior capability perform regression, classification, and clustering. Support vector regression (SVR) is a powerful suitable tool that been successfully used for forecasting. However, performance SVR model extremely dependent on optimal selection its hyper-parameters. In this paper, novel forecast based hybrid bald eagle search (BES) proposed short-term model, BES algorithm, which characterized by adjustable parameters, simplified mechanism, accurate results, enhance accuracy forecasted output optimizing hyper-parameters model. To evaluate developed forecaster, case study conducted real data from Sotavento Galicia Spain. compared other such as decision tree (DT), random forest (RF), traditional SVR, gray wolf optimization algorithm (SVR–GWO) manta ray foraging optimizer (SVR–MRFO). Obtained results uncovered SVR−BES more than methods.

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ژورنال

عنوان ژورنال: Journal of Engineering and Applied Science

سال: 2022

ISSN: ['2536-9512', '1110-1903']

DOI: https://doi.org/10.1186/s44147-022-00161-w