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

被引:87
作者
Peng, Yuzhen [1 ]
Rysanek, Adam [1 ]
Nagy, Zoltan [2 ]
Schluter, Arno [1 ]
机构
[1] Swiss Fed Inst Technol, Dept Architecture, Inst Technol Architecture, Architecture & Bldg Syst, Zurich, Switzerland
[2] Univ Texas Austin, Intelligent Environm Lab, Dept Civil Architectural & Environm Engn, Austin, TX 78712 USA
关键词
Occupancy learning; Occupancy prediction; Demand-driven control; HVAC; Energy efficiency; Intelligent systems; PERFORMANCE; BUILDINGS; SYSTEM;
D O I
10.1016/j.buildenv.2017.06.010
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:145 / 160
页数:16
相关论文
共 32 条
[1]  
Agarwal Yuvraj, 2010, P 2 ACM WORKSH EMB S, P1, DOI DOI 10.1145/1878431.1878433
[2]  
[Anonymous], SENSOR BASED OCCUPAN
[3]  
[Anonymous], 2013, TRANSITION SUSTAINAB, DOI DOI 10.1787/9789264202955-EN
[4]  
[Anonymous], 2011, P UBICOMP
[5]  
[Anonymous], 2019, ASHRAE HDB HVAC APPL
[6]  
BARBER D., 2012, Bayesian Reasoning and Machine Learning
[7]   Assessing building performance in use 2: technical performance of the Probe buildings [J].
Bordass, B ;
Cohen, R ;
Standeven, M ;
Leaman, A .
BUILDING RESEARCH AND INFORMATION, 2001, 29 (02) :103-113
[8]  
Braun J.E., 1990, ASHRAE TRAN, V96, P2
[9]   Achieving better energy-efficient air conditioning - A review of technologies and strategies [J].
Chua, K. J. ;
Chou, S. K. ;
Yang, W. M. ;
Yan, J. .
APPLIED ENERGY, 2013, 104 :87-104
[10]   Occupancy schedules learning process through a data mining framework [J].
D'Oca, Simona ;
Hong, Tianzhen .
ENERGY AND BUILDINGS, 2015, 88 :395-408