Machine Learning Approaches to Energy Consumption Forecasting in Households
This work addresses energy consumption forecasting for households, but it is incremental as it extends and compares existing methods without introducing new paradigms.
The paper tackles multi-step ahead power demand forecasting in residential micro-grids by extending existing ARMA, SVM, and RNN methods, finding that machine learning schemes achieve smaller prediction errors in mean and variance compared to ARMA, but no single algorithm is clearly superior.
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.