Building Energy Management Systems — Optimization of comfort and energy use

Building Energy Management Systems (BEMS) have been introduced in the built environment as a mechanism to increase the energy efficiency while maintaining the required comfort levels. BEMS also offers promising flexibility for Demand Side Management and Demand Responses (DSM&DR) to interact with Smart Grids. Through the use of intelligent control techniques, BEMS can be optimized to exploit the built environment' flexibility while ensuring the minimum comfort levels required by the building's user. This paper presents an analysis framework for BEMS based on a state of the art literature review. Furthermore, Simscape is introduced as a simulation tool that can model effectively sub-systems in the BEMS.

[1]  Yoonkee Kim,et al.  Building Energy Management System based on Smart Grid , 2011, 2011 IEEE 33rd International Telecommunications Energy Conference (INTELEC).

[2]  Lingfeng Wang,et al.  Development of multi-agent system for building energy and comfort management based on occupant behaviors , 2013 .

[3]  F. Sioshansi.  Demand-side management: The third wave , 1995 .

[4]  Wil L. Kling,et al.  An application of the successive shortest path algorithm to manage power in multi-agent system based active networks , 2010 .

[5]  Barbara Messing,et al.  An Introduction to MultiAgent Systems , 2002, Künstliche Intell..

[6]  Peng Zhao,et al.  An Energy Management System for Building Structures Using a Multi-Agent Decision-Making Control Methodology , 2010, 2010 IEEE Industry Applications Society Annual Meeting.

[7]  F. Obayashi,et al.  A study of building energy management system based of multi agents , 2003, SICE 2003 Annual Conference (IEEE Cat. No.03TH8734).

[8]  Jose Medina,et al.  Demand Response and Distribution Grid Operations: Opportunities and Challenges , 2010, IEEE Transactions on Smart Grid.

[9]  Bing Dong,et al.  Building energy and comfort management through occupant behaviour pattern detection based on a large-scale environmental sensor network , 2011 .

[10]  Goran Strbac,et al.  Demand side management: Benefits and challenges ☆ , 2008 .

[11]  Lingfeng Wang,et al.  Multi-zone building energy management using intelligent control and optimization , 2013 .

[12]  Paulo F. Ribeiro,et al.  Smart Power Router: A Flexible Agent-Based Converter Interface in Active Distribution Networks , 2011, IEEE Transactions on Smart Grid.

[14]  Anastasios I. Dounis,et al.  Advanced control systems engineering for energy and comfort management in a building environment--A review , 2009 .

[15]  Kecheng Liu,et al.  A Multi-Agent System for Building Control , 2006, 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology.

[16]  Kecheng Liu,et al.  Multi-agent Control of Shared Zones in Intelligent Buildings , 2008, 2008 International Conference on Computer Science and Software Engineering.

[17]  S.D.J. McArthur,et al.  Multi-Agent Systems for Power Engineering Applications—Part I: Concepts, Approaches, and Technical Challenges , 2007, IEEE Transactions on Power Systems.