Transfer learning based multi-layer extreme learning machine for probabilistic wind power forecasting

Yanli Liu and Junyi Wang

Applied Energy, 2022, vol. 312, issue C, No S0306261922001866

Abstract: With the increasing penetration of wind power, probabilistic forecasting becomes critical to quantifying wind power uncertainties and guiding power system operations. This paper proposes a transfer learning based probabilistic wind power forecasting method. Model-based transfer learning is utilized to construct the multi-layer extreme learning machine (MLELM). The output mapping factors of MLELM are further optimized through the particle swarm optimization (PSO) with the objective of minimizing the quantile evaluation indexes. Joint distribution adaptation (JDA) is utilized to update the weights of MLELM to accommodate variable wind power output. Test results on the practical wind farms in China shows that the proposed method can provide more accurate quantile forecasting results with better nonlinear fitting ability compared with other quantile forecasting methods.

Keywords: Extreme learning machine; Probabilistic wind power forecasting; Transfer learning (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (26)

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DOI: 10.1016/j.apenergy.2022.118729

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