Energy Saving by Using Internet of Things Paradigm and Machine Learning

Nowadays, energy consumption is acquiring growing attention for the economic and environmental implications in our society due to the growing number of electronic home devices. From this perspective, the Internet of Things (IoT) and Machine Learning have emerged as technologies that allow monitoring and controlling devices installed in houses to detect behavioral patterns that identify feasible scenarios of energy saving. For this reason, intelligent configuration approaches for home automation are of utmost importance. This paper proposes a mobile application (called IntelihOgarT) that optimizes energy consumption through Machine Learning and IoT, while improving, at the same time, comfort at home. The proposed application makes use of Machine Learning algorithm C4.5, which automatically takes decisions based on attributes of a training data set. Furthermore, the case study presented validates the effectiveness of the mobile application, where efficient use of energy at home is a primary concern.