Abstract
To accurately predict the amount of power generation, the particle swarm optimization grey season model with fractional order accumulation (PSO-FGSM(1,1) model) is proposed. Seasonal indices are introduced into the new model to enhance its seasonality, and particle swarm algorithm is used to find the optimal order. In order to evaluate the performance of the proposed model, the calculation results of the Holt-Winters model are used for comparison. The experimental results show that the prediction errors of the proposed model and Holt-Winters model are 2.4% and 3.93% respectively. It is proved that the new model has better predictive performance. Finally, the new model is discussed in two specific cases, which further reflect the prediction ability of the proposed model to predict seasonal data. The accurate prediction results can provide reference for the allocation of power resources.
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Acknowledgements
Acknowledgments of the National Natural Science Foundation of China (No.71871084), the Excellent Young Scientist Foundation of Hebei Education Department (No. SLRC2019001) and the project of high-level talent in Hebei province.
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The relevant researches in this paper are supported by the National Natural Science Foundation of China (No.71871084), the Excellent Young Scientist Foundation of Hebei Education Department (No. SLRC2019001) and the project of high-level talent in Hebei province.
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Lifeng Wu contributed to the conception and design of the study. Kai Zhang conducted material preparation, data collection and analysis. The first draft of the manuscript was written by Kai Zhang, and all authors reviewed early drafts of the manuscript. All authors read and approved the final manuscript.
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Zhang, K., Wu, L. Using Fractional Order Grey Seasonal Model to Predict the Power Generation in China. Environ. Process. 8, 413–427 (2021). https://doi.org/10.1007/s40710-020-00477-w
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DOI: https://doi.org/10.1007/s40710-020-00477-w