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Point and Interval Solar Power Forecasting Using Hybrid Empirical Wavelet Transform and Robust Wavelet Kernel Ridge Regression

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Abstract

In this paper, a new and efficient hybrid empirical wavelet transform (EWT)-based reduced robust Mexican hat wavelet kernel ridge regression (RMHWK) model is proposed to achieve both point and interval forecasting of solar power in a smart grid scenario. Initially, the actual nonlinear solar power data series was decomposed by the EWT method. A reduced robust kernel ridge regression (RKRR) approach was incorporated that shows a notable decrease in training time without appreciable loss in forecasting accuracy. The reduction in the size of the kernel matrix was achieved by selecting a set of random support vectors from the training data set. For validating the superior performance of the proposed EWT-RMHWK forecasting model, a numerical experimentation implementing a real-time data set of 1 MW solar power plant (Odisha, India) as well as an online historical data set (Florida, USA) was considered and compared with other hybrid models using either empirical mode decomposition- or wavelet decomposition-based RKRR and EWT-ELM, etc. The kernel parameters were optimized with the chaotic water cycle algorithm to boost the performance of the proposed prediction model. Further, the proposed EWT-RKRR method was used to construct prediction interval forecasting with three different confidence levels with 90%, 95%, and 99% for Florida solar power plant using different time horizons of 15 min, 1 h, and 1 day, respectively.

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Authors and Affiliations

  1. Siksha O Anusandhan (Deemed to be University), Bhubaneswar, India

    P. K. Dash, Irani Majumder, Niranjan Nayak & Ranjeeta Bisoi

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  1. P. K. Dash
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  2. Irani Majumder
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  4. Ranjeeta Bisoi
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Correspondence to Ranjeeta Bisoi.

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Dash, P.K., Majumder, I., Nayak, N. et al. Point and Interval Solar Power Forecasting Using Hybrid Empirical Wavelet Transform and Robust Wavelet Kernel Ridge Regression. Nat Resour Res 29, 2813–2841 (2020). https://doi.org/10.1007/s11053-020-09630-6

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