Title: Solar power forecasting using robust kernel extreme learning machine and decomposition methods

Authors: Irani Majumder; Ranjeeta Bisoi; Niranjan Nayak; Naeem Hannoon

Addresses: Department of Electrical Engineering, Institute of Technical Education and Research, Siksha O Anusandhan (Deemed to be University), Bhubaneswar-751030, Odisha, India ' Multidisciplinary Research Cell, Siksha O Anusandhan (Deemed to be University), Khandagiri Square, Bhubaneswar-751030, Odisha, India ' Department of Electrical and Electronics Engineering, Institute of Technical Education and Research, Siksha O Anusandhan (Deemed to be University), Bhubaneswar-751030, Odisha, India ' Faculty of Electrical Engineering, University Technology Mara, Kolej Amira Uitm Puncak Alam Road, 42300 Shah Alam, Selangor, Malaysia

Abstract: This paper proposes empirical mode decomposition (EMD)-based robust kernel extreme learning machine (RKELM) to achieve a precise predicted value of solar power generation in a smart grid environment. The non-stationary historical solar power data is initially decomposed into various intrinsic mode functions (IMFs) using EMD, which are subsequently passed through the proposed robust Morlet wavelet kernel extreme learning machine (RWKELM) for solar power prediction at different time horizons. Further a reduced kernel matrix version of RWKELM is used to decrease the training time significantly without appreciable loss of forecasting accuracy. By implementing the real time data for validation of the proposed method for short term solar power prediction it can be observed that the proposed EMD-based RWKELM outperforms various other methods, in terms of different performance matrices and execution time. The solar power prediction results on experimental data show the lowest error which proves the highest prediction accuracy.

Keywords: solar power forecasting; empirical mode decomposition; EMD; wavelet transform decomposition; WD; extreme learning machine; ELM; robust kernel extreme learning machine; RKELM; reduced kernel matrix.

DOI: 10.1504/IJPEC.2020.107958

International Journal of Power and Energy Conversion, 2020 Vol.11 No.3, pp.260 - 290

Received: 14 Nov 2018
Accepted: 14 Jan 2019
Published online: 01 Jul 2020 *

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