Title: Building a prediction model of solar power generation based on improved Grey Markov Chain

Authors: Chongyu Cui; Zhaoxia Li; Junjie Zhang

Addresses: Tibet Agricultural and Animal Husbandry University, Nyingchi, Tibet 86000, China ' Tibet Agricultural and Animal Husbandry University, Nyingchi, Tibet 86000, China ' State Grid Tibet Electric Power Company Limited Electric Power Research Institute, Xizang, Tibet, China

Abstract: In order to improve the prediction ability and reliability management ability of solar power generation, a solar power generation prediction model based on Improved Grey Markov chain is proposed. The constrained parameter model of solar power generation prediction is established, and the disturbance characteristics of solar power generation are analysed. On this basis, the improved grey Markov chain model is applied to the big data fusion analysis of solar power generation, and the reliability prediction of solar power generation is realised. The results show that the prediction accuracy of this method is high, up to 1, which improves the quality and stability of output power, and has certain application value.

Keywords: grey Markov chain; solar energy; electricity generation; prediction; charge volatility; power grid.

DOI: 10.1504/IJGEI.2022.121396

International Journal of Global Energy Issues, 2022 Vol.44 No.2/3, pp.139 - 149

Received: 07 Aug 2020
Accepted: 29 Oct 2020
Published online: 10 Mar 2022 *

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