Intelligent Monitoring for Efficient Use of Energy in Buildings

Efficient use of energy in buildings has become a major concern of a sustainable society. Minor shifts in peak demand have major implications in terms of savings for both consumers and utilities. This study develops a cloud computing-based big data analytics framework for a building energy efficiency monitoring (BEEM) system that enables managers to facilitate efficient energy use in multiple residential buildings. The framework incorporates smart meter technology, remote sensing and monitoring, Bluetooth technology, big data analytics, cloud computing technology, optimization algorithm, web-based information technology, and electricity pricing policies. Initial experiment was performed to verify potential benefits of the proposed framework. Particularly, a smart grid infrastructure and sensors were installed in a building to retrieve data. An intelligent machine learning-based prediction model was developed to predict future usage of energy in buildings. The BEEM system can support managers and encourage the end-users to effectively monitor daily and monthly energy usage of buildings.