An innovative architecture for an intelligent building energy management system

Abstract

Due to the increasing urbanization one of the main focuses of the environmental policy is constituted by the cities, where a better quality of life and lower energy consumption will be made possible by digital technology and innovation. Buildings are one of the main urban sectors involved in this challenge. The management systems of Smart Buildings look beyond the general objective to fulfill the occupants’ comfort requirements and reduce the energy consumption. Smart Buildings are connected and responsive to the Smart Power Grid, and they interact with building operators and occupants to empower them with new monitoring levels and operational information on building performance. This work proposes a modular and hierarchical system architecture for the building energy management that accounts for the building operating conditions and the surrounding grid system. The research experiences described in this work constitute modules of this system acting at different levels and with different purposes. The first research experience is related to the regulation of the indoor temperatures of a multi-zones building on the basis of the occupancy profiles through an adaptive model predictive control law. Secondly a comprehensive fault detection and diagnosis methodology of anomalous building energy consumptions through artificial intelligence and data mining techniques is presented. Finally the results obtained by the application of this fault detection and diagnosis methodology to the electrical consumptions of several actual buildings are described.