利用鲸鱼优化法改进指数平滑灰螺栓电价预测模型

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES MethodsX Pub Date : 2024-09-01 DOI:10.1016/j.mex.2024.102926
Benjamin Salomon Diboma , Flavian Emmanuel Sapnken , Mohammed Hamaidi , Yong Wang , Prosper Gopdjim Noumo , Jean Gaston Tamba
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引用次数: 0

摘要

本研究介绍了一种开创性的方法,即基于鲸鱼优化算法(WOA)的多变量指数平滑格雷-霍尔特(GMHES)模型,该模型专为电价预测而设计。WOA-GMHES(1,N) 模型的主要特点包括:利用历史数据理解电价的基本趋势,利用 WOA 算法对模型参数进行自适应优化,以捕捉不断变化的市场动态。在喀麦隆真实的高压和低压电价数据基础上对该模型进行评估,证明其优于其他竞争模型。WOA-GMHES(1,N) 模型的 RMSE 和 SMAPE 分数分别为 12.63% 和 0.01%,表现出了卓越的性能,证明了其准确性和可靠性。WOA-GMHES(1,N) 模型因其计算效率高、预测快速准确而脱颖而出,成为能源行业时效性决策的重要工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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.

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来源期刊
MethodsX
MethodsX Health Professions-Medical Laboratory Technology
CiteScore
3.60
自引率
5.30%
发文量
314
审稿时长
7 weeks
期刊介绍:
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