Improved exponential smoothing grey-holt models for electricity price forecasting using whale optimization.

Autor:	Diboma BS; Higher Institute of Transport, Logistics and Commerce, PO Box 22, University of Ebolowa, Ambam, Cameroon., Sapnken FE; Higher Institute of Transport, Logistics and Commerce, PO Box 22, University of Ebolowa, Ambam, Cameroon.; University of Douala University Institute of Technology, PO Box 8698, Douala, Cameroon.; Energy Insight-Tomorrow Today, PO Box 2043 Douala, Cameroon., Hamaidi M; Departement of mathematics, Faculty of exact sciences and computer science, Ziane Achour University of Djelfa, Algeria., Wang Y; School of Sciences, Southwest Petroleum University, Chengdu, Sichuan 610500, PR China., Noumo PG; Higher Institute of Transport, Logistics and Commerce, PO Box 22, University of Ebolowa, Ambam, Cameroon.; University of Douala University Institute of Technology, PO Box 8698, Douala, Cameroon., Tamba JG; Higher Institute of Transport, Logistics and Commerce, PO Box 22, University of Ebolowa, Ambam, Cameroon.; University of Douala University Institute of Technology, PO Box 8698, Douala, Cameroon.; Energy Insight-Tomorrow Today, PO Box 2043 Douala, Cameroon.
Jazyk:	angličtina
Zdroj:	MethodsX [MethodsX] 2024 Sep 01; Vol. 13, pp. 102926. Date of Electronic Publication: 2024 Sep 01 (Print Publication: 2024).
DOI:	10.1016/j.mex.2024.102926
Abstrakt:	This study introduces a ground-breaking approach, the Whale Optimization Algorithm (WOA)-based multivariate exponential smoothing Grey-Holt (GMHES) model, designed for electricity price forecasting. Key features of the proposed WOA-GMHES(1,N) model include leveraging historical data to comprehend the underlying trends in electricity prices and utilizing the WOA algorithm for adaptive optimization of model parameters to capture evolving market dynamics. Evaluating the model on authentic high- and low-voltage electricity price data from Cameroon demonstrates its superiority over competing models. The WOA-GMHES(1,N) model achieves remarkable performance with RMSE and SMAPE scores of 12.63 and 0.01 %, respectively, showcasing its accuracy and reliability. Notably, the model proves to be computationally efficient, generating forecasts in <1.3 s. Three key aspects of customization distinguish this novel approach:•The WOA algorithm dynamically adjusts model parameters based on evolving electricity market dynamics.•The model employs a sophisticated GMHES approach, considering multiple factors for a comprehensive understanding of price trends.•The WOA-GMHES(1,N) model stands out for its computational efficiency, providing rapid and precise forecasts, making it a valuable tool for time-sensitive decision-making in the energy sector. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2024 The Author(s).)
Databáze:	MEDLINE
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