Occupancy learning-based demand-driven cooling control for office spaces

Abstract Occupancy in buildings is one of the key factors influencing air-conditioning energy use. Occupant presence and absence are stochastic. However, static operation schedules are widely used by facility departments for air-conditioning systems in commercial buildings. As a result, such systems cannot adapt to actual energy demand for offices that are not fully occupied during their operating time. This study analyzes a seven-month period of occupancy data based on motion signals collected from six offices with ten occupants in a commercial building, covering both private and multi-person offices. Based on an occupancy analysis, a learning-based demand-driven control strategy is proposed for sensible cooling. It predicts occupants' next presence and the presence duration of the remainder of a day by learning their behavior in the past and current days, and then the predicted occupancy information is employed indirectly to infer setback temperature setpoints according to rules we specified in this study. The strategy is applied for the controls of a cooling system using passive chilled beams for sensible cooling of office spaces. Over the period of two months both a baseline control and the proposed demand-driven control were operated on forty-two weekdays of real-world occupancy. Using the demand-driven control, an energy saving of 20.3% was achieved as compared to the benchmark. We found that energy savings potential in an individual office was inversely correlated to its occupancy rate.

[1]  H. Mirinejad,et al.  A review of intelligent control techniques in HVAC systems , 2012, 2012 IEEE Energytech.

[2]  W Wim Zeiler,et al.  Occupancy measurement in commercial office buildings for demand-driven control applications : a survey and detection system evaluation , 2015 .

[3]  H. B. Gunay,et al.  Development of an occupancy learning algorithm for terminal heating and cooling units , 2015 .

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

[5]  Silvia Santini,et al.  Predicting household occupancy for smart heating control: A comparative performance analysis of state-of-the-art approaches , 2014 .

[6]  James E. Braun,et al.  Reducing energy costs and peak electrical demand through optimal control of building thermal storage , 1990 .

[7]  John Krumm,et al.  PreHeat: controlling home heating using occupancy prediction , 2011, UbiComp '11.

[8]  Arno Schlueter,et al.  Occupant centered lighting control for comfort and energy efficient building operation , 2015 .

[9]  Bing Dong,et al.  Sensor-based occupancy behavioral pattern recognition for energy and comfort management in intelligent buildings , 2009 .

[10]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[11]  J. H. Eto.  A COMPARISON OF WEATHER NORMALIZATION TECHNIQUES FOR COMMERICAL BUILDING ENERGY USE , 1985 .

[12]  Tuan Anh Nguyen,et al.  Energy intelligent buildings based on user activity: A survey , 2013 .

[13]  António E. Ruano,et al.  The IMBPC HVAC system: A complete MBPC solution for existing HVAC systems , 2016 .

[14]  Kamin Whitehouse,et al.  The smart thermostat: using occupancy sensors to save energy in homes , 2010, SenSys '10.

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

[16]  David Barber,et al.  Bayesian reasoning and machine learning , 2012 .

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

[18]  O. T. Masoso,et al.  The dark side of occupants’ behaviour on building energy use , 2010 .

[19]  Tianzhen Hong,et al.  Occupancy schedules learning process through a data mining framework , 2015 .

[20]  Geoffrey Qiping Shen,et al.  Occupancy data analytics and prediction: A case study , 2016 .

[21]  Arno Schlueter,et al.  3for2: Realizing spatial, material, and energy savings through integrated design , 2016 .

[22]  Siaw Kiang Chou,et al.  Achieving better energy-efficient air conditioning - A review of technologies and strategies , 2013 .

[23]  Adrian Leaman,et al.  Assessing building performance in use 3: energy performance of the Probe buildings , 2001 .

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

[25]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[26]  Peter Harrington,et al.  Machine Learning in Action , 2012 .