A model-based control strategy to recover cooling energy from thermal mass in commercial buildings

Abstract Building structures and furniture have the capability of storing thermal energy and therefore could be called building thermal mass. In commercial buildings, room conditions remain in thermal comfort zone for a while after the end of office hours when the air-conditioning systems are tuned off. It is possible to recover the stored cooling energy from building thermal mass in commercial buildings. This study proposes a model-based control strategy for such purpose. A simplified building RC model and a black-box model are combined by a simple data fusion algorithm for easier implementation and more accuracy prediction. The feasibility of such energy recovery was validated on-site in a super high-rise commercial building in Hong Kong, and the proposed method was validated on a dynamic simulation platform built based on the same building. In the two on-site validations, the energy savings during the energy recovering period were 85.8% and 80.1%, respectively. In the simulation tests, by allowing indoor air temperature increase by 1 K, the proposed control strategy could save 83.8% of the cooling energy during the recovering period, which accounts for 7.23% of the total cooling energy consumption in the entire tested days.

[1]  J. Braun,et al.  Load Control Using Building Thermal Mass , 2003 .

[2]  James E. Braun,et al.  Evaluation of methods for determining demand-limiting setpoint trajectories in buildings using short-term measurements , 2008 .

[3]  Shengwei Wang,et al.  Dynamic simulation of a building central chilling system and evaluation of EMCS on-line control strategies , 1998 .

[4]  E. Wurtz,et al.  Assessment of an algorithm involving building thermal mass, indoor and outdoor mean running temperatures to enhance the control of night ventilation , 2018 .

[5]  O. M. Brastein,et al.  Parameter estimation for grey-box models of building thermal behaviour , 2018, Energy and Buildings.

[6]  Frank P. Incropera,et al.  Fundamentals of Heat and Mass Transfer , 1981 .

[7]  James E. Braun,et al.  A Simplified Method for Determining Optimal Cooling Control Strategies for Thermal Storage in Building Mass , 1996 .

[8]  Na Zhu,et al.  Dynamic characteristics and energy performance of buildings using phase change materials: A review , 2009 .

[9]  Yongjun Sun,et al.  Development and In-situ validation of a multi-zone demand-controlled ventilation strategy using a limited number of sensors , 2012 .

[10]  J. Braun,et al.  Model-based demand-limiting control of building thermal mass , 2008 .

[11]  Norhayati Mahyuddin,et al.  Concrete as a thermal mass material for building applications - A review , 2018, Journal of Building Engineering.

[12]  Peizheng Ma,et al.  Modeling of Thermal Mass in a Small Commercial Building and Potential Improvement by Applying TABS , 2015 .

[13]  Shengwei Wang,et al.  Building demand response and control methods for smart grids: A review , 2016 .

[14]  Shengwei Wang,et al.  Parameter estimation of internal thermal mass of building dynamic models using genetic algorithm , 2006 .

[15]  Zhe Zhang,et al.  A state-space thermal model incorporating humidity and thermal comfort for model predictive control in buildings , 2018, Energy and Buildings.

[16]  C. Y. Chung,et al.  Economic MPC of Aggregating Commercial Buildings for Providing Flexible Power Reserve , 2015, IEEE Transactions on Power Systems.

[17]  Francesco Leccese,et al.  Passive thermal behaviour of buildings: Performance of external multi-layered walls and influence of internal walls , 2018, Applied Energy.

[18]  Shengwei Wang,et al.  In-situ validation of a fault tolerant control strategy for VAV systems , 2015 .

[19]  William D'haeseleer,et al.  Integrated modeling of active demand response with electric heating systems coupled to thermal energy storage systems , 2015 .

[20]  Peng Xu.  Demand Shifting with Thermal Mass in Light and Heavy Mass Commercial Buildings , 2009 .

[21]  Aidan Reilly,et al.  The impact of thermal mass on building energy consumption , 2017 .

[22]  Shengwei Wang,et al.  Energy efficient design and control of cleanroom environment control systems in subtropical regions – A comparative analysis and on-site validation , 2017 .

[23]  Alan S. Fung,et al.  Modelling for performance prediction of highly insulated buildings with different types of thermal mass , 2017 .

[24]  Charlotte Ellerbrok.  Potentials of Demand Side Management Using Heat Pumps with Building Mass as a Thermal Storage , 2014 .

[25]  M. Piette,et al.  Peak Demand Reduction from Pre-Cooling with Zone Temperature Reset in an Office Building , 2004 .

[26]  Xing Lu,et al.  A new method for calculating the thermal effects of irregular internal mass in buildings under demand response , 2016 .

[27]  Amaryllis Audenaert,et al.  Thermal inertia in buildings: A review of impacts across climate and building use , 2018 .

[28]  Kyoung-ho Leea,et al.  Development of methods for determining demand-limiting setpoint trajectories in buildings using short-term measurements , 2007 .