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

Yang Yang and Xiuqin Wang

Chaos, Solitons & Fractals, 2022, vol. 158, issue C

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.

Keywords: Grey model; Conformable fractional-order calculus; Time-delay model; Optimized model; Autoregression error estimation (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077922002144
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:158:y:2022:i:c:s0960077922002144

DOI: 10.1016/j.chaos.2022.112004

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().