Machine Learning Approaches to Energy Consumption Forecasting in Households

TL;DR

This paper compares various machine learning models for multi-step ahead energy demand forecasting in households, highlighting their relative accuracy and proposing a hybrid approach tailored to specific use case requirements.

Contribution

It extends existing models to multi-step forecasting, implements an efficient training framework, and evaluates multiple machine learning techniques on real data.

Findings

ML models outperform ARMA in prediction accuracy

No single best algorithm; performance varies by use case

Hybrid approach recommended based on prediction interval and error

Abstract

We consider the problem of power demand forecasting in residential micro-grids. Several approaches using ARMA models, support vector machines, and recurrent neural networks that perform one-step ahead predictions have been proposed in the literature. Here, we extend them to perform multi-step ahead forecasting and we compare their performance. Toward this end, we implement a parallel and efficient training framework, using power demand traces from real deployments to gauge the accuracy of the considered techniques. Our results indicate that machine learning schemes achieve smaller prediction errors in the mean and the variance with respect to ARMA, but there is no clear algorithm of choice among them. Pros and cons of these approaches are discussed and the solution of choice is found to depend on the specific use case requirements. A hybrid approach, that is driven by the prediction interval, the target error, and its uncertainty, is then recommended.