The coupled effects of personalized occupancy profile based HVAC schedules and room reassignment on building energy use

Abstract Buildings account for nearly 38% of the total energy use in the U.S., and 46% of this use is associated with commercial buildings. More than 40% of the energy in commercial buildings is consumed by HVAC systems, which provide heating, cooling and ventilation to individual zones to maintain comfortable and healthy indoor environments. A more refined control strategy based on actual occupancy might improve HVAC system related energy efficiency. Accurate occupancy profiles are important to determine actual energy demands and corresponding control schedules. This paper focuses on energy efficiency in office buildings with centrally controlled VAV systems by setting zone-level HVAC start/stop schedules using personalized occupancy profiles, which represent occupants’ long-term presence patterns. Evaluation of the method was performed using a simulation model, calibrated by the actual energy use data of an office test bed building. Up to 9% of the energy was saved when personalized occupancy profile based HVAC schedules were used. However, if occupants of a zone have different occupancy patterns, the aggregated patterns may hinder any potential efficiency that might be realized from zone-level HVAC start/stop schedules. This paper also presents an approach for reassigning rooms to unify the start/stop times at the zone level by placing occupants with similar profiles in the same mechanical zones. When room reassignment was implemented and coupled with profile based control schedule, HVAC energy use was reduced by another 8%. The proposed methods could provide reliable personalized HVAC control for small-size office buildings without advanced building automation systems, nevertheless, they could also be extended to buildings with packaged HVAC systems and individual air-conditioning systems.

[1]  Alberto Cerpa,et al.  Occupancy based demand response HVAC control strategy , 2010, BuildSys '10.

[2]  Manfred Morari,et al.  Importance of occupancy information for building climate control , 2013 .

[3]  Alberto E. Cerpa,et al.  Energy efficient building environment control strategies using real-time occupancy measurements , 2009, BuildSys '09.

[4]  Darren Robinson,et al.  A generalised stochastic model for the simulation of occupant presence , 2008 .

[5]  Carlos Duarte,et al.  Revealing occupancy patterns in an office building through the use of occupancy sensor data , 2013 .

[6]  Refrigerating.  ASHRAE handbook of fundamentals , 1967 .

[7]  Daniel Macumber,et al.  OpenStudio: An Open Source Integrated Analysis Platform , 2011 .

[8]  S. T. Taylor,et al.  HVAC systems and equipment , 1990 .

[9]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[10]  David E. Claridge,et al.  Electricity Diversity Profiles for Energy Simulation of Office Buildings , 2004 .

[11]  Ardeshir Mahdavi,et al.  MODELING USER CONTROL OF LIGHTING AND SHADING DEVICES IN OFFICE BUILDINGS: AN EMPIRICAL CASE STUDY , 2007 .

[12]  Francis Rubinstein,et al.  Modeling occupancy in single person offices , 2005 .

[13]  James E. Braun,et al.  A methodology for estimating occupant CO2 source generation rates from measurements in small commercial buildings , 2007 .

[14]  Vice President,et al.  AMERICAN SOCIETY OF HEATING, REFRIGERATION AND AIR CONDITIONING ENGINEERS INC. , 2007 .

[15]  David E. Culler,et al.  Reducing Transient and Steady State Electricity Consumption in HVAC Using Learning-Based Model-Predictive Control , 2012, Proceedings of the IEEE.

[16]  Ardeshir Mahdavi.  Patterns and Implications of User Control Actions in Buildings , 2009 .

[17]  Thomas Weng,et al.  Occupancy-driven energy management for smart building automation , 2010, BuildSys '10.

[18]  Prabir Barooah,et al.  Effect of various uncertainties on the performance of occupancy-based optimal control of HVAC zones , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[19]  Francesco Borrelli,et al.  A distributed predictive control approach to building temperature regulation , 2011, Proceedings of the 2011 American Control Conference.

[20]  M. Zaheer-Uddin,et al.  Optimization of thermal processes in a variable air volume HVAC system , 1996 .

[21]  Tianzhen Hong,et al.  Statistical analysis and modeling of occupancy patterns in open-plan offices using measured lighting-switch data , 2013 .

[22]  Zheng Yang,et al.  Modeling personalized occupancy profiles for representing long term patterns by using ambient context , 2014 .

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

[24]  Yi Jiang,et al.  A novel approach for building occupancy simulation , 2011 .

[25]  Ardeshir Mahdavi,et al.  169: Observation-based models of user control actions in buildings , 2008 .

[26]  Tianzhen Hong,et al.  Occupant Behavior: Impact onEnergy Use of Private Offices , 2013 .

[27]  M Morari,et al.  Energy efficient building climate control using Stochastic Model Predictive Control and weather predictions , 2010, Proceedings of the 2010 American Control Conference.

[28]  George J. Pappas,et al.  Receding-horizon supervisory control of green buildings , 2011, Proceedings of the 2011 American Control Conference.

[29]  Bing Dong,et al.  Integrated building control based on occupant behavior pattern detection and local weather forecasting , 2011 .

[30]  Gregor P. Henze,et al.  Evaluation of optimal control for active and passive building thermal storage , 2004 .

[31]  Miguel Á. Carreira-Perpiñán,et al.  OBSERVE: Occupancy-based system for efficient reduction of HVAC energy , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[32]  M. Zaheer-uddin,et al.  Dynamic simulation of energy management control functions for HVAC systems in buildings , 2006 .

[33]  Atila Novoselac,et al.  Localized air-conditioning with occupancy control in an open office , 2010 .

[34]  Jlm Jan Hensen,et al.  Overview of HVAC system simulation , 2010 .

[35]  Sean P. Meyn,et al.  Building thermal model reduction via aggregation of states , 2010, Proceedings of the 2010 American Control Conference.

[36]  Rhys Goldstein,et al.  Schedule-calibrated occupant behavior simulation , 2010, SpringSim.

[37]  B. Efron.  Better Bootstrap Confidence Intervals , 1987 .

[38]  James A. Davis,et al.  Occupancy diversity factors for common university building types , 2010 .

[39]  Burcin Becerik-Gerber,et al.  A systematic approach to occupancy modeling in ambient sensor-rich buildings , 2014, Simul..

[40]  Zhenjun Ma,et al.  Supervisory and Optimal Control of Building HVAC Systems: A Review , 2008 .

[41]  Kamin Whitehouse,et al.  The self-programming thermostat: optimizing setback schedules based on home occupancy patterns , 2009, BuildSys '09.