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Energies 2018, 11(1), 68; https://doi.org/10.3390/en11010068

Hourly Solar Radiation Forecasting Using a Volterra-Least Squares Support Vector Machine Model Combined with Signal Decomposition

Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
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Received: 27 November 2017 / Revised: 17 December 2017 / Accepted: 18 December 2017 / Published: 1 January 2018
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Abstract

Accurate solar forecasting facilitates the integration of solar generation into the grid by reducing the integration and operational costs associated with solar intermittencies. A novel solar radiation forecasting method was proposed in this paper, which uses two kinds of adaptive single decomposition algorithm, namely, empirical mode decomposition (EMD) and local mean decomposition (LMD), to decompose the strong non-stationary solar radiation sequence into a set of simpler components. The least squares support vector machine (LSSVM) and the Volterra model were employed to build forecasting sub-models for high-frequency components and low-frequency components, respectively, and the sub-forecasting results of each component were superimposed to obtain the final forecast results. The historical solar radiation data collected on Golden (CO, USA), in 2014 were used to evaluate the accuracy of the proposed model and its comparison with that of the ARIMA, the persistent model. The comparison demonstrated that the superior performance of the proposed hybrid method. View Full-Text
Keywords: hourly solar radiation; empirical mode decomposition; local mean decomposition; least squares support vector machine; Volterra model; hybrid model hourly solar radiation; empirical mode decomposition; local mean decomposition; least squares support vector machine; Volterra model; hybrid model
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Wang, Z.; Tian, C.; Zhu, Q.; Huang, M. Hourly Solar Radiation Forecasting Using a Volterra-Least Squares Support Vector Machine Model Combined with Signal Decomposition. Energies 2018, 11, 68.

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