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王德运,乐陈强(外),Adnen ElAmraoui(外)Multi-step-ahead electricity load forecasting using a novel hybrid architecture 王德运,乐陈强(外),Adnen ElAmraoui(外)Multi-step-ahead electricity load forecasting using a novel hybrid architecture

发布人:陈永佳 发布时间:2022-09-23 点击次数:

王德运,乐陈强(外),Adnen ElAmraoui(外)Multi-step-ahead electricity load forecasting using a novel hybrid architecture with decomposition-based error correction strategy.

我校经济管理学院王德运老师在T3级别期刊——《Chaos, Solitons and Fractals: the interdisciplinary journal of Nonlinear Science》上发表题为“Multi-step-ahead electricity load forecasting using a novel hybrid architecture with decomposition-based error correction strategy”。论文第一作者王德运为经济管理学院副教授,博士生导师。

Abstract / 摘要

MT 本研究提出了一种混合学习范式与误差修正策略相结合的多步前电力负荷预测新架构。提出架构的详细内容如下:( 1 )开发了一种基于互补集成经验模式分解( CEEMD )和粒子群优化( PSO-BP )改进的BP神经网络的混合学习范式,用于电力负荷的初步预测;( 2 )建立了基于变分模态分解( VMD )和PSO - BP的误差预测方法,用于后续误差的预测;( 3 )利用误差预测模型的预测结果对初步预测值进行标定。具体而言,在误差修正过程中,将原始数据序列分解成三个子集,生成合理的历史误差序列用于建立误差预测模型。基于PJM和安大略电力市场的数据,提出并调查了两个案例研究,以评估所提架构的有效性。评估结果表明,与本研究考虑的其他基准模型相比,本文提出的架构能够产生更高精度的结果。

原文 In this study, a novel architecture combining a hybrid learning paradigm and an error correction strategy is presented for multi-step-ahead electricity load forecasting. The detail of the proposed architecture is provided as follows: (1) a novel hybrid learning paradigm based on complementary ensemble empirical mode decomposition (CEEMD) and backpropagation (BP) neural network improved by particle swarm optimization (PSO-BP) is developed for preliminary prediction of the electricity load; (2) an error prediction approach based on variational mode decomposition (VMD) and PSO-BP is established for prediction of the subsequent error; (3) calibrate the preliminary prediction values using the forecast results of the error prediction model. Specifically, in the error correction process, the original data series is separated into three subsets to generate a reasonable historical error series used for establishing the error prediction model. Two case studies based on the data of PJM and Ontario electricity markets are presented and investigated to assess the effectiveness of the proposed architecture. The evaluation results demonstrate that the proposed architecture can yield results in higher accuracy than other benchmark models considered in this study.

论文信息;

Title/题目:

Multi-step-ahead electricity load forecasting using a novel hybrid architecture with decomposition-based error correction strategy

Authors/作者:

Wang Deyun;Yue Chenqiang;ElAmraoui Adnen

Key Words / 关键词

Electricity load;Multi-step-ahead forecasting;Error correction strategy;Time series decomposition;Hybrid forecasting model

Indexed by / 核心评价

Scopus; WAJCI; EI; SCI; INSPEC;

Highlights /研究要点

• A decomposition-based error correction procedure is established for improving the overall forecast accuracy.;

• The BP model optimized by PSO algorithm obtains better forecasting performance.;

• A novel hybrid architecture for multi-step-ahead electricity load forecasting is proposed.;

• The proposed hybrid architecture is tested using two real-world data series with different volatility characteristics.

DOI:10.1016/J.CHAOS.2021.111453

全文链接:https://linkinghub.elsevier.com/retrieve/pii/S0960077921008079