A Data-Driven Methodology for Heating Optimization in Smart Buildings

In the paradigm of Internet of Things new applications that leverage ubiquitous connectivity enable - together with Big Data Analytics - the emergence of Smart City initiatives. This paper proposes to build a closed loop data modeling methodology in order to optimize energy consumption in a fundamental smart city scenario: smart buildings. This methodology is based on the fusion of information about relevant parameters affecting energy consumption in buildings, and the application of recommended big data techniques in order to improve knowledge acquisition for better decision making and ensure energy efficiency. Experiments carried out in different buildings demonstrate the suitability of the proposed methodology.