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

Abstract

Electric Load Forecasting (ELF) is the central instrument for planning and controlling demand response programs, electricity trading, and consumption optimization. Due to the increasing automation of these processes, meaningful and transparent forecasts become more and more important. Still, at the same time, the complexity of the used machine learning models and architectures increases.

Because 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 Learning. Based 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 forecasts.

The 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 models. Dominant explainable approaches are Feature Importance and Attention mechanisms. The discussion shows that a lot of knowledge from the related field of time series forecasting still needs to be adapted to the problems in ELF. Compared to other applications of explainable and interpretable methods such as clustering, there are currently relatively few research results, but with an increasing trend.