An explainable machine learning approach for energy forecasting at the household level

TL;DR

A custom decision tree is introduced, aiming at providing a fair estimate of the energy consumption, while being explainable and consistent with human intuition, and it is shown that this novel method allows greater explainability without sacrificing much accuracy.

Abstract

Electricity forecasting has been a recurring research topic, as it is key to finding the right balance between production and consumption. While most papers are focused on the national or regional scale, few are interested in the household level. Desegregated forecast is a common topic in Machine Learning (ML) literature but lacks explainability that household energy forecasts require. This paper specifically targets the challenges of forecasting electricity use at the household level. This paper confronts common Machine Learning algorithms to electricity household forecasts, weighing the pros and cons, including accuracy and explainability with well-known key metrics. Furthermore, we also confront them in this paper with the business challenges specific to this sector such as explainability or outliers resistance. We introduce a custom decision tree, aiming at providing a fair estimate of the energy consumption, while being explainable and consistent with human intuition. We show that this novel method allows greater explainability without sacrificing much accuracy. The custom tree methodology can be used in various business use cases but is subject to limitations, such as a lack of resilience with outliers.

Figures (9)

• Figure 1: Strengths and drawbacks of each method
• Figure 2: Features importance for Gradient Boosting and Random Forest Regressor models
• Figure 3: Possible decision tree architecture
• Figure 4: Key metrics for each model
• Figure 5: Gaps distribution for each model in absolute value