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Explainability and Interpretability in Electric Load Forecasting Using Machine Learning Techniques–A review

Energy and AI 2024年第2期16卷 483-496页

作者： Lukas Baur Konstantin Ditschuneit Maximilian Schambach Can Kaymakci Thomas Wollmann Alexander Sauer Fraunhofer Institute for Manufacturing Engineering and Automation IPA Nobelstraße 12Stuttgart70569Germany Institute for Energy Efficiency in Production(EEP) University of StuttgartNobelstraße 12Stuttgart70569Germany Merantix Momentum GmbH Max-Urich-Straße 3Berlin13355Germany

Electric Load Forecasting(ELF)is the central instrument for planning and controlling demand response programs,electricity trading,and consumption *** to the increasing automation of these processes,meaningful and transparent forecasts become more and more ***,at the same time,the complexity of the used machine learning models and architectures *** there is an increasing interest in interpretable and explainable load forecasting methods,this work conducts a literature review to present already applied approaches regarding explainability and interpretability for load forecasts using Machine *** on extensive literature research covering eight publication portals,recurring modeling approaches,trends,and modeling techniques are identified and clustered by properties to achieve more interpretable and explainable load *** results on interpretability show an increase in the use of probabilistic models,methods for time series decomposition and the use of fuzzy logic in addition to classically interpretable *** explainable approaches are Feature Importance and Attention *** discussion shows that a lot of knowledge from the related field of time series forecasting still needs to be adapted to the problems in *** to other applications of explainable and interpretable methods such as clustering,there are currently relatively few research results,but with an increasing trend.

关键词： Electric load forecasting Explainability interpretabilitystructured review

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