Alternative ventilation strategies in U.S. offices: Comprehensive assessment and sensitivity analysis of energy saving potential

Abstract Mature technologies exist to reduce the heating, ventilation, and air-conditioning (HVAC) energy associated with ventilation and use ventilation proactively to save energy. This study investigated the energy use impacts in U.S. office buildings of multiple alternative ventilation strategies that combined: economizing, demand controlled ventilation (DCV), supply air temperature reset (SR), and/or a doubled ventilation rate. We used energy simulations in a Monte Carlo analysis, sampling 17 building inputs and varying locations to match the climate zone distribution of the U.S. office stock. Results indicated the possibility for significant savings compared to a baseline that ventilated constantly at a minimum rate in both a small office type with a constant air volume (CAV) HVAC system and a medium office type with a variable air volume (VAV) system. In 95% of instances, HVAC source energy savings were 5–25% in the small-CAV office (median: 11%) and 6–42% in the medium-VAV office (median: 27%). In the small-CAV office, DCV typically saved the most energy, usually from heating, and heating degree days and occupant density were decisive influences. In the medium-VAV office, economizing and SR were most important, DCV usually only had minor impacts, and zone temperature setpoints, along with climate indicators, were the critical influences. Other than infiltration, envelope characteristics did not strongly influence energy impacts. The untapped primary energy savings of alternative ventilation strategies over the 74% of U.S. office floorspace reasonably represented by our modeling was estimated at 36 TWh per year, with an annual value of U.S. $1.25 billion.

[1]  A Rackes,et al.  Do time-averaged, whole-building, effective volatile organic compound (VOC) emissions depend on the air exchange rate? A statistical analysis of trends for 46 VOCs in U.S. offices. , 2016, Indoor air.

[2]  Wei Tian,et al.  A review of sensitivity analysis methods in building energy analysis , 2013 .

[3]  Saltelli Andrea,et al.  Global Sensitivity Analysis: The Primer , 2008 .

[4]  Ye Yao,et al.  Energy analysis on VAV system with different air-side economizers in China , 2010 .

[5]  Tom Ben-David,et al.  Impact of natural versus mechanical ventilation on simulated indoor air quality and energy consumption in offices in fourteen U.S. cities , 2016 .

[6]  Gregor P. Henze,et al.  Uncertainty quantification for combined building performance and cost-benefit analyses , 2013 .

[7]  Bing Liu,et al.  U.S. Department of Energy Commercial Reference Building Models of the National Building Stock , 2011 .

[8]  Adams Rackes,et al.  Modeling impacts of dynamic ventilation strategies on indoor air quality of offices in six US cities , 2013 .

[9]  Adams Rackes,et al.  Using multiobjective optimizations to discover dynamic building ventilation strategies that can improve indoor air quality and reduce energy use , 2014 .

[10]  M. J. Brandemuehl,et al.  The impact of demand-controlled and economizer ventilation strategies on energy use in buildings , 1999 .

[11]  Nabil Nassif,et al.  A robust CO2-based demand-controlled ventilation control strategy for multi-zone HVAC systems , 2012 .

[12]  Jlm Jan Hensen,et al.  Uncertainty analysis in building performance simulation for design support , 2011 .

[13]  G. Henze,et al.  SAMPLING BASED ON SOBOL 0 SEQUENCES FOR MONTE CARLO TECHNIQUES APPLIED TO BUILDING SIMULATIONS , 2011 .

[14]  Frances Y. Kuo,et al.  Remark on algorithm 659: Implementing Sobol's quasirandom sequence generator , 2003, TOMS.

[15]  A. Saltelli,et al.  Importance measures in global sensitivity analysis of nonlinear models , 1996 .

[16]  William J. Fisk,et al.  Changing ventilation rates in U.S. offices: Implications for health, work performance, energy, and associated economics , 2012 .

[17]  Ronald E. Jarnagin,et al.  Weighting Factors for the Commercial Building Prototypes Used in the Development of ANSI/ASHRAE/IESNA Standard 90.1-2010 , 2010 .

[18]  Marjorie Musy,et al.  Application of sensitivity analysis in building energy simulations: combining first and second order elementary effects Methods , 2012, ArXiv.

[19]  Zheng O'Neill,et al.  A methodology for meta-model based optimization in building energy models , 2012 .

[20]  Andrew K. Persily,et al.  Indoor air quality analyses of commercial reference buildings , 2012 .

[21]  M Hamilton,et al.  Perceptions in the U.S. building industry of the benefits and costs of improving indoor air quality. , 2016, Indoor air.

[22]  Anibal T. de Almeida,et al.  Sensor-based demand-controlled ventilation: a review , 1998 .

[23]  Paul Bratley,et al.  Algorithm 659: Implementing Sobol's quasirandom sequence generator , 1988, TOMS.

[24]  Gang Wang,et al.  Air handling unit supply air temperature optimal control during economizer cycles , 2012 .

[25]  J. Samet,et al.  Ventilation rates and health: multidisciplinary review of the scientific literature. , 2011, Indoor air.

[26]  W. Fisk,et al.  Economic benefits of an economizer system: Energy savings and reduced sick leave , 2004 .

[27]  J. E. Janssen,et al.  Ventilation for acceptable indoor air quality , 1989 .

[28]  Ana Paula Melo,et al.  Naturally comfortable and sustainable: Informed design guidance and performance labeling for passive commercial buildings in hot climates , 2016 .

[29]  Wanyu Rengie Chan Assessing the effectiveness of shelter -in -place as an emergency response to large-scale outdoor chemical releases , 2006 .

[30]  Jan F. Kreider,et al.  Heating and Cooling of Buildings : Design for Efficiency, Revised Second Edition , 2009 .

[31]  C. Chao,et al.  Development of a dual-mode demand control ventilation strategy for indoor air quality control and energy saving , 2004 .

[32]  O Seppänen,et al.  Ventilation and performance in office work. , 2006, Indoor air.

[33]  P. Green,et al.  Analyzing multivariate data , 1978 .

[34]  M S Waring,et al.  Real‐time transformation of outdoor aerosol components upon transport indoors measured with aerosol mass spectrometry , 2017, Indoor air.

[35]  Michael C. Baechler,et al.  Building America Best Practices Series: Volume 7.1: Guide to Determining Climate Regions by County , 2010 .

[36]  José Manuel Cejudo López,et al.  Uncertainties and sensitivity analysis in building energy simulation using macroparameters , 2013 .