Evolution of Building Energy Management Systems for greater sustainability through explainable artificial intelligence models

Abstract

Predicting energy consumption is a task that allows energy supply companies to adapt to certain behaviours. The activities that companies can undertake include learning about the behaviour of their customers in order to adapt their tariffs to consumption or identifying the intervals in which there will be a higher demand for energy and to plan for the adaptation of supply chains. While predicting energy consumption is no longer a major challenge, and models with high accuracy rates have been developed, an clear understanding of energy consumption among users is still obscure. If the problem of explainability is resolved, companies will be able to better adapt their services by generating the exact amount of energy to be sold, which will also reduce its cost for customers. There is no single explanatory approach to learning models that works best. There are multiple paths to achieving explainability: data vs. model, directly interpretable vs. post hoc explanation, local vs. global, etc. This article reviews which explainable artificial intelligence algorithms are the most appropriate for a given use case, as multiple forms of explanation can lead to confusion in figuring out which algorithms are the most appropriate for a given use case. In our case study, a specific dataset, extracted from a two-year period in a shoe store, is used to review some of the main explainable artificial intelligence algorithms on machine learning models, capable of predicting energy consumption and subsequently providing explainability to the process.