Introduction to the Special Section on Applying Machine Learning Systems for IoT Services in Industrial Informatics

Abstract

Energy consumption has increased in recent decades at a rate ranging from 1.5% to 10% per year in the developed world. As a consequence, several efforts have been made to model energy consumption in order to achieve a better use of energy and to minimize environmental impact. Open problems in this area range from energy consumption forecasting to user profile mining, energy source planning, to transportation, among others. To address these problems, it is important to have suitable tools to model energy consumption data series, so that the analysts and CEOs can have knowledge about the underlying properties of the power demand in order to make high-level decisions. In this paper, we focus on the problem of energy consumption modelling, and provide a solution from the perspective of symbolic regression. More specifically, we develop hybrid genetic programming algorithms to find the algebraic expression that best models daily energy consumption in public buildings at the University of Granada as a testbed, and compare the benefits of Straight Line Programs with the classic tree representation used in symbolic regression. Regarding algorithm design, the outcomes of our experimentation suggest that Straight Line Programs outperform other representation models in the symbolic regression problems studied, and also that the hybridation with local search methods can improve the quality of the resulting algebraic expression. On the other hand, with regards to energy consumption modelling, our approach empirically demonstrates that symbolic regression can be a powerful tool to find underlying relationships between multivariate energy consumption data series.