A new method to solve large-scale building energy management for energy saving

Building energy saving has attracted more and more attention around the world recently. However, there are many challenges and difficulties in optimization for building energy saving due to a large amount of rooms in the buildings. So in this paper, we provide a method to manage the building energy systems for energy saving. In particularly, the proposed method is effective that can be applied to the large-scale buildings. We make the following major contributions. First, an iterative and decentralized solution method is developed. Based on this framework, the optimization problem of building energy management is divided into three sub-optimization problems. This method iteratively solves the three sub-problems to obtain the solution of the building energy management problem. Second, the three sub-problems are converted into mixed integer programming problems, respectively, and they can be solved using CPLEX. Numerical examples are used to demonstrate the performance of the method.

[1]  Vladislav Kantchev Shunturov,et al.  Dormitory residents reduce electricity consumption when exposed to real‐time visual feedback and incentives , 2007 .

[2]  Qing-Shan Jia,et al.  Energy-Efficient Buildings Facilitated by Microgrid , 2010, IEEE Transactions on Smart Grid.

[3]  Jim Kurose,et al.  GreenCharge : Managing Renewable Energy in Smart Buildings , 2012 .

[4]  Richard Bull,et al.  The use of building energy certificates to reduce energy consumption in European public buildings. , 2012 .

[5]  Peter B. Luh,et al.  Building Energy Management: Integrated Control of Active and Passive Heating, Cooling, Lighting, Shading, and Ventilation Systems , 2013, IEEE Transactions on Automation Science and Engineering.

[6]  Marcelo Gradella Villalva,et al.  Modeling and circuit-based simulation of photovoltaic arrays , 2009, 2009 Brazilian Power Electronics Conference.

[7]  Frauke Oldewurtel,et al.  Building modeling as a crucial part for building predictive control , 2013 .

[8]  Jili Zhang,et al.  Development of an energy monitoring system for large public buildings , 2013 .

[9]  Jim Kurose,et al.  GreenCharge: Managing RenewableEnergy in Smart Buildings , 2013, IEEE Journal on Selected Areas in Communications.

[10]  Ping Jiang,et al.  Overcoming barriers to implementation of carbon reduction strategies in large commercial buildings in China , 2010 .

[11]  Qing-Shan Jia,et al.  Supply demand coordination for building energy saving , 2013, 2013 IEEE International Conference on Automation Science and Engineering (CASE).

[12]  C. F. Kettleborough,et al.  A Review of Desiccant Cooling Systems , 1993 .

[13]  M. M. Gouda,et al.  Building thermal model reduction using nonlinear constrained optimization , 2002 .

[14]  L. T. Terziotti,et al.  Modeling seasonal solar thermal energy storage in a large urban residential building using TRNSYS 16 , 2012 .