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A novel modified conformable fractional grey time-delay model for power generation prediction

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  • Yang, Yang
  • Wang, Xiuqin

Abstract

Many fractional-order grey models are proposed and discussed for electric power generation data analysis, which are helpful in enterprise production and policy scheduling. Considering that time-delay is a universal phenomenon in real life and engineering application, a new and comprehensive conformable fractional grey time-delay model is established by extending classical grey models with the forms of conformable fractional derivative, conformable fractional accumulation and delay parameters. Considering delay data is always unknown, Lagrange interpolation is used to estimate the time-delay data. Compared with linear estimation, high order Lagrange interpolation will provide more detail information in the fitting stage. Furthermore, the optimal and modified models are also disused for predicting the future power generation by the tested data in this paper. The errors between the simulated and real data were analyzed and predicted by autoregression model, which is good at revealing the inner trend for historical residuals. The accuracies of modeling and forecasting can be improved by the optimization algorithm and autoregression error estimation in this paper. The results show the optimal and modified models could be widely used in forecasting electrical time series data, which has high effectiveness and flexibility. The novel model could enrich the connotation of parameters and the physical significance of the traditional fractional grey model.

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  • Yang, Yang & Wang, Xiuqin, 2022. "A novel modified conformable fractional grey time-delay model for power generation prediction," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    • Handle: RePEc:eee:chsofr:v:158:y:2022:i:c:s0960077922002144
    DOI: 10.1016/j.chaos.2022.112004
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    References listed on IDEAS

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    Cited by:

    1. Wang, Xiaolei & Xie, Naiming & Yang, Lu, 2022. "A flexible grey Fourier model based on integral matching for forecasting seasonal PM2.5 time series," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).

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