The target of smart houses and enhanced living environments is to increase the quality of life further. In this context, more supporting platforms for smart houses were developed, some of them using cloud systems for remote supervision, control and data storage. An important aspect, which is an open issue for both industry and academia, is represented by how to reduce and estimate energy consumption for a smart house. In this paper, we propose a modular platform that uses the power of cloud services to collect, aggregate and store all the data gathered from the smart environment. Then, we use the data to generate advanced neural network models to create energy awareness by advising the smart environment occupants on how they can improve daily habits while reducing the energy consumption and thus also the costs.
|Titolo:||Deep learning model for home automation and energy reduction in a smart home environment platform|
|Data di pubblicazione:||2019|
|Appare nelle tipologie:||1.1 Articolo in rivista|