High dimensional very short-term solar power forecasting based on a data-driven heuristic method

Amir Rafati, Mahmood Joorabian, Elaheh Mashhour and Hamid Reza Shaker

Energy, 2021, vol. 219, issue C

Abstract: Improving the accuracy of solar power forecasting has become crucial for dealing with the negative effects of the integration of continually increasing solar power into power systems. This is a more challenging task when historical solar radiation data has not been recorded and no specific sky imaging equipment is available. This paper proposes a univariate data-driven method to improve the accuracy of very short-term electrical solar power forecasting. This approach includes defining new features that efficiently tackle the nonlinear characteristics of electrical solar power. An instance-based variable selection is also used to identify the best relevant features. Three state-of-the-art learning algorithms (i.e. neural networks, support vector regression, and random forest) have been used and compared as prediction algorithms of the proposed method. The effectiveness of the proposed approach is evaluated in a 15-min ahead prediction trial using a real solar power dataset and against three evaluation measures. The results show the proposed method significantly enhances the performance of very short-term solar power forecasting.

Keywords: Solar photovoltaic power; Very short-term forecasting; Feature selection; Neural networks; Support vector regression; Random forests (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:219:y:2021:i:c:s0360544220327547

DOI: 10.1016/j.energy.2020.119647

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