因果关系发现和因果导向方法在产消行为和需求灵活性预测中的增强绩效和可解释性

IF 2 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Mingyue He, Mojdeh Khorsand
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引用次数: 0

摘要

因果分析为复杂系统和现象的更可解释的评估铺平了道路,例如能源系统的人在环组件。本文将采用新的方法对产消者行为进行因果分析。这种因果关系的知识是多种智能电网应用的核心,包括但不限于需求侧管理程序的设计,零售电力市场的设计,有效的分布式能源聚合策略的开发,以及净负荷预测。人类与能源相互作用的复杂性依赖于许多因素,理解行为因果关系是一个核心的、未解决的挑战。本文提出了一种发现终端用户消费灵活性及其影响因素之间因果关系的概率算法。然后利用所获得的因果知识来提高需求灵活性预测的精度。提出了两种面向因果的方法,通过因果正则化和数据预处理将因果信息纳入预测模型,以提高预测模型的性能和可解释性。仿真结果表明,该算法能够有效地识别出不同因素之间的因果概率,揭示出产消者行为的关键特征。此外,这些提出的因果导向方法在性能和可解释性方面都优于非因果导向的预测模型,突出了纳入因果信息的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Causal relationship discovery and causal-oriented approaches for enhanced performance and interpretability in prediction of prosumer behavior and demand flexibility

Causal relationship discovery and causal-oriented approaches for enhanced performance and interpretability in prediction of prosumer behavior and demand flexibility

Causal analysis paves the way for more interpretable assessment of complex systems and phenomena, such as human-in-the-loop components of energy systems. This article will pursue novel approaches for causal analysis of prosumers' behavior. The knowledge of this causality is core for multiple smart grid applications including but not limited to the design of demand side management programs, retail electricity market design, development of effective distributed energy resources aggregation strategies, and net load forecasting. The complex nature of human interactions with energy relies on many factors and understanding behavior causality is a core, unsolved challenge. This article presents a probabilistic algorithm for discovering causal relationships between the end users' consumption flexibility and its influencing factors. The obtained causal knowledge is then utilized to boost the precision of demand flexibility prediction. Two causal-oriented approaches are proposed to enhance the performance and interpretability of predictive models, incorporating causal information through causal regularization and data preprocessing. Simulation results demonstrate that the algorithm can effectively identify causal probabilities among different factors and unveil key characteristics of the prosumers' behavior. Additionally, these proposed causal-oriented approaches outperform the non-causal-oriented predictive models in terms of both performance and interpretability, highlighting the advantages of incorporating causal information.

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来源期刊
Iet Generation Transmission & Distribution
Iet Generation Transmission & Distribution 工程技术-工程:电子与电气
CiteScore
6.10
自引率
12.00%
发文量
301
审稿时长
5.4 months
期刊介绍: IET Generation, Transmission & Distribution is intended as a forum for the publication and discussion of current practice and future developments in electric power generation, transmission and distribution. Practical papers in which examples of good present practice can be described and disseminated are particularly sought. Papers of high technical merit relying on mathematical arguments and computation will be considered, but authors are asked to relegate, as far as possible, the details of analysis to an appendix. The scope of IET Generation, Transmission & Distribution includes the following: Design of transmission and distribution systems Operation and control of power generation Power system management, planning and economics Power system operation, protection and control Power system measurement and modelling Computer applications and computational intelligence in power flexible AC or DC transmission systems Special Issues. Current Call for papers: Next Generation of Synchrophasor-based Power System Monitoring, Operation and Control - https://digital-library.theiet.org/files/IET_GTD_CFP_NGSPSMOC.pdf
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