A case study on understanding energy consumption through prediction and visualization (VIMOEN)

Energy efficiency has emerged as an overarching concern due to the high pollution and cost associated with operating heating, ventilation and air-conditioning systems in buildings, which are an essential part of our day to day life. Besides, energy monitoring becomes one of the most important research topics nowadays as it enables us the possibility of understanding the consumption of the facilities. This, along with energy forecasting, represents a very decisive task for energy efficiency. The goal of this study is divided into two parts. First to provide a methodology to predict energy usage every hour. To do so, several Machine Learning technologies were analysed: Trees, Support Vector Machines and Neural Networks. Besides, as the University of Granada lacks a tool to properly monitoring those data, a second aim is to propose an intelligent system to visualize and to use those models in order to predict energy consumption in real-time. To this end, we designed VIMOEN (VIsual MOnitoring of ENergy), a web-based application to provide not only visual information about the energy consumption of a set of geographically-distributed buildings but also expected expenditures in the near future. The system has been designed to be easy-to-use and intuitive for non-expert users. Our system was validated on data coming from buildings of the UGR and the experiments show that the Elman Neural Networks proved to be the most accurate and stable model and since the 5th hour the results maintain accuracy.