Home // International Journal On Advances in Software, volume 6, numbers 3 and 4, 2013 // View article
Authors:
Ignacio González Alonso
María Rodríguez Fernández
Juan Jacobo Peralta
Adolfo Cortés García
Keywords: Digital Home; Energy Efficiency; Smart Metering; Cloud; Big Data Analytics
Abstract:
Improving the energy efficiency is one of the most effective ways to increase supply security and reduce Green House Gasses emissions. Furthermore, the increased cost of energy has encouraged the development of new technologies that allow the efficient use of them, such as monitoring the final users energy demand; hence, it is possible to have a more efficient consumption behavior without lowering the threshold of comfort that consumers are used to. The Smart Home Energy project makes a profitable use of these technologies by allowing the final user to manage, control, plan and, in most cases, reduce the electric bill. To facilitate the bidirectional interaction between the customer and the devices integrated in the smart home communication network it is necessary to follow a holistic approach. This proposal aims to go a step further by using the massive consumption datasets to predict future energy behaviors, offering personalized recommendations and developing a customized “consumer energy knowledge” for every home, through heavily processed Machine Learning algorithms.
Pages: 261 to 271
Copyright: Copyright (c) to authors, 2013. Used with permission.
Publication date: December 31, 2013
Published in: journal
ISSN: 1942-2628