Building energy management decision-making in the real world: A comparative study of HVAC cooling strategies

Abstract In recent years, various heating, ventilation, and air-conditioning (HVAC) control strategies have been proposed to reduce buildings’ energy consumption and environmental impacts. Due to different research designs, it is impossible to directly compare the reported performance of these strategies and determine the best approach for operating real-world buildings. To fill this gap, this research uses a well-validated building simulation model to holistically evaluate the performance of 12 existing HVAC cooling strategies in reducing the total cooling energy use, peak demand, and cooling energy cost. The findings include: i) The ON/OFF and night setup control strategies could lead to even higher energy cost than the 24/7 operation baseline; ii) demand-limiting control methods performed well across the three energy performance metrics and achieved the highest energy reduction ratios ranging from 9.8% to 10.5%; iii) precooling and extended precooling reduced the peak load most significantly by 15.4% and 21.4%, respectively; and iv) the resistive-capacitive (RC) network-based precooling optimization resulted in the highest electricity cost savings with reduction ratios of 16.6% and 12.9%, based on the two common price schedules. Besides providing valuable insights to the research community, this study offers a practical method to help building operators analyze and select the best HVAC control strategies for their energy management goals.

[1]  Jing Li,et al.  ThermoNet: Fine-Grain Assessment of Building Comfort and Efficiency , 2012, ANT/MobiWIS.

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

[3]  Jon Hand,et al.  CONTRASTING THE CAPABILITIES OF BUILDING ENERGY PERFORMANCE SIMULATION PROGRAMS , 2008 .

[4]  Farrokh Janabi-Sharifi,et al.  Theory and applications of HVAC control systems – A review of model predictive control (MPC) , 2014 .

[5]  Moncef Krarti,et al.  Assessment of natural and hybrid ventilation models in whole-building energy simulations , 2011 .

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

[7]  António E. Ruano,et al.  Neural networks based predictive control for thermal comfort and energy savings in public buildings , 2012 .

[8]  Shengwei Wang,et al.  A model-based optimal ventilation control strategy of multi-zone VAV air-conditioning systems , 2009 .

[9]  Qian Chen,et al.  An RC-Network Approach for HVAC Precooling Optimization in Buildings , 2019 .

[10]  Kazuhide Ito,et al.  Field-based study on the energy-saving effects of CO2 demand controlled ventilation in an office with application of Energy recovery ventilators , 2014 .

[11]  Guohui Gan,et al.  Numerical simulation of the indoor environment , 1994 .

[12]  Rubiyah Yusof,et al.  Review of HVAC scheduling techniques for buildings towards energy-efficient and cost-effective operations , 2013 .

[13]  Arthur L. Dexter,et al.  A simplified physical model for estimating the average air temperature in multi-zone heating systems , 2004 .

[14]  Abbas Abbassi,et al.  Application of neural network for the modeling and control of evaporative condenser cooling load , 2005 .

[15]  John Ingersoll,et al.  Heating energy use management in residential buildings by temperature control , 1985 .

[16]  Jin Woo Moon,et al.  Thermostat strategies impact on energy consumption in residential buildings , 2011 .

[17]  Fred Bauman,et al.  Localized comfort control with a desktop task conditioning system: laboratory and field measurements , 1993 .

[18]  Jianhui Wang,et al.  MPC-Based Appliance Scheduling for Residential Building Energy Management Controller , 2013, IEEE Transactions on Smart Grid.

[19]  Arnaud G. Malan,et al.  HVAC control strategies to enhance comfort and minimise energy usage , 2001 .

[20]  Frauke Oldewurtel,et al.  Experimental analysis of model predictive control for an energy efficient building heating system , 2011 .

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

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

[23]  Jia Liu,et al.  HVAC Precooling Optimization for Green Buildings: An RC-Network Approach , 2018, e-Energy.

[24]  Naoaki Yamanaka,et al.  Distributed demand scheduling method to reduce energy cost in smart grid , 2013, 2013 IEEE Region 10 Humanitarian Technology Conference.

[25]  Elias Salleh,et al.  Evaluating thermal effects of internal courtyard in a tropical terrace house by computational simula , 2011 .

[26]  Morad R. Atif,et al.  Comparison between computed and field measured thermal parameters in an atrium building , 1998 .

[27]  Peng Xu,et al.  Demand reduction in building energy systems based on economic model predictive control , 2012 .

[28]  Jan Hensen,et al.  Thermal comfort in residential buildings: Comfort values and scales for building energy simulation , 2009 .

[29]  James E. Braun,et al.  Application of building precooling to reduce peak cooling requirements , 1997 .

[30]  J. Frame Locational marginal pricing , 2001, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[31]  Rongxin Yin Study on Auto-DR and Pre-Cooling of Commercial Buildings with Thermal Mass in California , 2010 .