Abstract

This paper introduces an innovative ventilation system that is capable of providing localized and customized thermal conditions in buildings. The system has diffusers with individually operable flaps that facilitate asymmetric air inlet to control air flow inside a room in an effective way. Moreover, the system involves distributed temperature sensors, a user interface, and a control unit that allows creation and management of “thermal subzones” within a room in accordance with the different preferences of occupants. As a specific case, the thermal management of a typical office in an academic building is considered. Both experimental and numerical studies were conducted to show that it is possible to achieve several degrees of temperature differences at different room locations in a transient and controllable fashion. The dynamic management of the temperature distribution in a room can prevent the waste of conditioning energy. It is shown that the system provides a practical and impactful solution by adapting to different user preferences (UPs) and by minimizing the resource use. In order to deal with the complexity of design, development, and operation of the system, it is considered as a cyber-physical-social system (CPSS). The core of the CPSS approach used here is an enhanced hybrid system modeling methodology that couples human dimension with formal hybrid dynamical modeling. Based on a coherent conceptual framing, the approach can combine the three core aspects, like cyber infrastructure, physical dynamics, and social/human interactions of modern building energy systems to accommodate the environmental challenges. Besides physics-based achievements (managing temperature distribution inside a room), the new AVS can also leverage user engagement and behavior change for energy efficiency in buildings by facilitating a new practice for occupants' interaction with heating, ventilation, and air conditioning (HVAC) system.

References

1.
Burkhard
,
J.
, and
Kadakia
,
R.
,
2008
, “
WBCSD Summary Report: Energy Efficiency in Buildings
,” Geneva, Switzerland,
Report
.https://www.wbcsd.org/contentwbc/download/2480/30566
2.
IEA
,
2015
,
World Energy Outlook 2015
,
International Energy Agency
,
Paris, France
.
3.
IEA
,
2018
,
The Future of Cooling Opportunities for Energy-Efficient Air Conditioning Together Secure Sustainable
,
International Energy Agency
,
Paris, France
.
4.
Huang
,
H.
,
Chen
,
L.
, and
Hu
,
E.
,
2015
, “
A Neural Network-Based Multi-Zone Modelling Approach for Predictive Control System Design in Commercial Buildings
,”
Energy Build.
,
97
, pp.
86
97
.10.1016/j.enbuild.2015.03.045
5.
Afram
,
A.
,
Janabi-Sharifi
,
F.
,
Fung
,
A. S.
, and
Raahemifar
,
K.
,
2017
, “
Artificial Neural Network (ANN) Based Model Predictive Control (MPC) and Optimization of HVAC Systems: A State of the Art Review and Case Study of a Residential HVAC System
,”
Energy Build.
,
141
, pp.
96
113
.10.1016/j.enbuild.2017.02.012
6.
Zhang
,
R.
,
Lam
,
K. P.
,
Yao
,
S.
, and
Zhang
,
Y.
,
2013
, “
Coupled EnergyPlus and Computational Fluid Dynamics Simulation for Natural Ventilation
,”
Build. Environ.
,
68
, pp.
100
113
.10.1016/j.buildenv.2013.04.002
7.
D'Oca
,
S.
,
Chen
,
C. F.
,
Hong
,
T.
, and
Belafi
,
Z.
,
2017
, “
Synthesizing Building Physics With Social Psychology: An Interdisciplinary Framework for Context and Occupant Behavior in Office Buildings
,”
Energy Res. Soc. Sci.
,
34
, pp.
240
251
.10.1016/j.erss.2017.08.002
8.
Loomans
,
M. G. L. C.
,
1998
,
The Measurement and Simulation of Indoor Air Flow
,
Technische Universiteit Eindhoven
, Department of the Built Environment, Eindhoven, The Netherlands.
9.
Liu
,
S.
, and
Novoselac
,
A.
,
2015
, “
Air Diffusion Performance Index (ADPI) of Diffusers for Heating Mode
,”
Build. Environ.
,
87
, pp.
215
223
.10.1016/j.buildenv.2015.01.021
10.
ASHRAE
,
2009
,
ASHRAE Handbook, Fundamentals
,
ASHRAE
,
Atlanta, GA
.
11.
Murai
,
A.
,
Yamaguchi
,
Y.
, and
Shimoda
,
Y.
,
2012
, “
Evaluation of Energy-Saving Performance of Office Building Task/Ambient Systems Considering Dynamic Worker's Behaviour
,”
IBPSA ASIM Proceedings
, Shanghai, China, pp.
3
10
.
12.
Bauman
,
F. S.
, and
Arens
,
E. A.
,
1996
, “
Task/Ambient Conditioning Systems: Engineering and Application Guidelines
,” Lawrence Berkeley National Laboratory, Research Project on Efficient Systems for Thermal Energy Distribution to California, Berkeley, CA, Report No. 4902510.
13.
Nielsen
,
P. V.
,
2007
, “
Analysis and Design of Room Air Distribution Systems
,”
HVACR Res.
,
13
(
6
), pp.
987
997
.10.1080/10789669.2007.10391466
14.
Srebric
,
J.
, and
Chen
,
Q.
,
2002
, “
Simplified Numerical Models for Complex Air Supply Diffusers
,”
HVACR Res.
,
8
(
3
), pp.
277
294
.10.1080/10789669.2002.10391442
15.
Tripathi
,
B.
, and
Moulic
,
S. G.
,
2012
, “
Investigation of Air Drafting Pattern Obtained From the Variation in Outlet Positions Inside a Closed Area
,”
J. Appl. Fluid Mech.
,
5
(
4
), pp.
1
12
. http://jafmonline.net/web/guest/home?p_p_id=JournalArchive_WAR_JournalArchive_INSTANCE_nvhn&p_p_action=0&p_p_state=maximized&p_p_mode=view&p_p_col_id=column-2&p_p_col_pos=3&p_p_col_count=6&_JournalArchive_WAR_JournalArchive_INSTANCE_nvhn_form_page=main_form&selectedVolumeId=62
16.
Sun
,
Y.
, and
Smith
,
T. F.
,
2005
, “
Air Flow Characteristics of a Room With Square Cone Diffusers
,”
Build. Environ.
,
40
(
5
), pp.
589
600
.10.1016/j.buildenv.2004.07.018
17.
Chua
,
K. J.
,
Chou
,
S. K.
,
Yang
,
W. M.
, and
Yan
,
J.
,
2013
, “
Achieving Better Energy-Efficient Air Conditioning—A Review of Technologies and Strategies
,”
Appl. Energy
,
104
, pp.
87
104
.10.1016/j.apenergy.2012.10.037
18.
Lin
,
H.
,
Koutsoukos
,
X. D.
, and
Antsaklis
,
P. J.
,
2003
, “
HYSTAR: A Toolbox for Hierarchical Control of Piecewise Linear Hybrid Dynamical Systems
,”
Proceedings of the 2002 American Control Conference
, Anchorage, AK, May 8–10, pp.
686
691
.10.1109/ACC.2002.1024892
19.
Goebel
,
R.
,
Sanfelice
,
R. G.
, and
Teel
,
A. R.
,
2009
, “
Hybrid Dynamical Systems
,”
IEEE Internet Comput. Syst. Mag.
,
29
(
2
), pp.
28
93
.10.1109/MCS.2008.931718
20.
Poveda
,
J. I.
,
Benosman
,
M.
, and
Teel
,
A. R.
,
2019
, “
Hybrid Online Learning Control in Networked Multiagent Systems: A Survey
,”
Int. J. Adapt. Control Signal Process.
,
33
(
2
), pp.
228
261
.10.1002/acs.2866
21.
Sanfelice
,
R. G.
,
2016
, “
Analysis and Design of Cyber-Physical Systems: A Hybrid Control Systems Approach
,”
Cyber-Physical Systems: From Theory to Practice
,
R.
Baheti
, and
H.
Gill
, eds.,
CRC Press
,
Boca Raton, FL
, pp.
3
32
.
22.
Meng
,
X.
,
Wen
,
Z.
, and
Qian
,
Y.
,
2018
, “
Multi-Agent Based Simulation for Household Solid Waste Recycling Behavior
,”
Resour. Conserv. Recycl.
,
128
, pp.
535
545
.10.1016/j.resconrec.2016.09.033
23.
Spandagos
,
C.
, and
Ng
,
T. L.
,
2018
, “
Fuzzy Model of Residential Energy Decision-Making Considering Behavioral Economic Concepts
,”
Appl. Energy
,
213
, pp.
611
625
.10.1016/j.apenergy.2017.10.112
24.
Setiawan
,
R. P.
,
Kaneko
,
S.
, and
Kawata
,
K.
,
2019
, “
Impacts of Pecuniary and Non-Pecuniary Information on pro-Environmental Behavior: A Household Waste Collection and Disposal Program in Surabaya City
,”
Waste Manag.
,
89
, pp.
322
335
.10.1016/j.wasman.2019.04.015
25.
Mahdavi
,
A.
, and
Tahmasebi
,
F.
,
2015
, “
Predicting People's Presence in Buildings: An Empirically Based Model Performance Analysis
,”
Energy Build.
,
86
, pp.
349
355
.10.1016/j.enbuild.2014.10.027
26.
Hawarah
,
L.
,
Ploix
,
S.
, and
Jacomino
,
M.
,
2010
,
User Behavior Prediction in Energy Consumption in Housing Using Bayesian Networks
(Lecture Notes in Computer Science), ICAISC 2010, Part-I, LNAI 6113, Springer-Verlag, Berlin, pp.
372
379
.
27.
Erickson
,
V. L.
,
Lin
,
Y.
,
Kamthe
,
A.
,
Brahme
,
R.
,
Surana
,
A.
,
Cerpa
,
A. E.
,
Sohn
,
M. D.
, and
Narayanan
,
S.
,
2009
, “
Energy Efficient Building Environment Control Strategies Using Real-Time Occupancy Measurements
,”
Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Build
(
BuildSys
'09), Berkeley, CA, Nov. 3, p.
19
.https://dl.acm.org/citation.cfm?id=1810284
28.
Somogyi
,
Z.
,
2016
, “
A Framework for Quantifying Environmental Sustainability
,”
Ecol. Indic.
,
61
, pp.
338
345
.10.1016/j.ecolind.2015.09.034
29.
Gu
,
P.
,
Xue
,
D.
, and
Nee
,
A. Y. C.
,
2009
, “
Adaptable Design: Concepts, Methods, and Applications
,”
Proc. Inst. Mech. Eng., Part B
,
223
(
11
), pp.
1367
1387
.10.1243/09544054JEM1387
30.
Li
,
Z.
, and
Dong
,
B.
,
2017
, “
A New Modeling Approach for Short-Term Prediction of Occupancy in Residential Buildings
,”
Build. Environ.
,
121
, pp.
277
290
.10.1016/j.buildenv.2017.05.005
31.
Yohanis
,
Y. G.
,
Mondol
,
J. D.
,
Wright
,
A.
, and
Norton
,
B.
,
2008
, “
Real-Life Energy Use in the UK: How Occupancy and Dwelling Characteristics Affect Domestic Electricity Use
,”
Energy Build.
,
40
(
6
), pp.
1053
1059
.10.1016/j.enbuild.2007.09.001
32.
Guerra-Santin
,
O.
, and
Itard
,
L.
,
2010
, “
Occupants' Behaviour: Determinants and Effects on Residential Heating Consumption
,”
Build. Res. Inf.
,
38
(
3
), pp.
318
338
.10.1080/09613211003661074
33.
Carpino
,
C.
,
Mora
,
D.
,
Arcuri
,
N.
, and
De Simone
,
M.
,
2017
, “
Behavioral Variables and Occupancy Patterns in the Design and Modeling of Nearly Zero Energy Buildings
,”
Build. Simul.
,
10
(
6
), pp.
875
888
.10.1007/s12273-017-0371-2
34.
Liedtke
,
C.
,
Baedeker
,
C.
,
Hasselkuß
,
M.
,
Rohn
,
H.
, and
Grinewitschus
,
V.
,
2015
, “
User-Integrated Innovation in Sustainable Living Labs: An Experimental Infrastructure for Researching and Developing Sustainable Product Service Systems
,”
J. Clean. Prod.
,
97
, pp.
106
116
.10.1016/j.jclepro.2014.04.070
35.
Jung
,
W.
, and
Jazizadeh
,
F.
,
2019
, “
Human-in-the-Loop HVAC Operations: A Quantitative Review on Occupancy, Comfort, and Energy-Efficiency Dimensions
,”
Appl. Energy
,
239
, pp.
1471
1508
.10.1016/j.apenergy.2019.01.070
36.
Keskin
,
C.
, and
Mengüç
,
M. P.
,
2018
, “
On Occupant Behavior and Innovation Studies Towards High Performance Buildings: A Transdisciplinary Approach
,”
Sustainability
,
10
(
10
), p.
3567
.10.3390/su10103567
37.
Wang
,
Y.
, and
Shao
,
L.
,
2017
, “
Understanding Occupancy Pattern and Improving Building Energy Efficiency Through Wi-Fi Based Indoor Positioning
,”
Build. Environ.
,
114
, pp.
106
117
.10.1016/j.buildenv.2016.12.015
38.
Gunay
,
B. H.
,
O'Brien
,
W.
, and
Beausoleil-Morrison
,
I.
,
2015
, “
Development of an Occupancy Learning Algorithm for Terminal Heating and Cooling Units
,”
Build. Environ.
,
93
(
P2
), pp.
71
85
.10.1016/j.buildenv.2015.06.009
39.
Brager
,
G. S.
,
Zhang
,
H.
, and
Arens
,
E.
,
2015
, “
Evolving Opportunities for Providing Thermal Comfort
,”
Build. Res. Inf.
,
43
(
3
), pp.
274
287
.10.1080/09613218.2015.993536
40.
Lunze
,
J.
, and
Lamnabhı-Lagarrıgue
,
F.
, eds.,
2009
,
Handbook of Hybrid Systems Control
,
Cambridge University Press
,
New York
.
41.
Chen
,
X.
,
Wang
,
Q.
, and
Srebric
,
J.
,
2015
, “
Model Predictive Control for Indoor Thermal Comfort and Energy Optimization Using Occupant Feedback
,”
Energy Build.
,
102
, pp.
357
369
.10.1016/j.enbuild.2015.06.002
42.
Ajib
,
B.
,
Lefteriu
,
S.
,
Caucheteux
,
A.
,
Lecoeuche
,
S.
,
Ajib
,
B.
,
Lefteriu
,
S.
,
Caucheteux
,
A.
, and
Lecoeuche
,
S.
,
2018
, “
Building Thermal Modeling Using a Hybrid System Approach
,”
20th World Congress the International Federation of Automatic Control
, Toulouse, France, July 9–14, pp.
10716
10720
.https://hal.archives-ouvertes.fr/hal-01720374/document
43.
Torrisi
,
F. D.
, and
Bemporad
,
A.
,
2004
, “
HYSDEL—A Tool for Generating Computational Hybrid Models
,”
IEEE Trans. Control Syst. Technol.
,
12
(
2
), pp.
235
249
.10.1109/TCST.2004.824309
44.
Bemporad
,
A.
,
2019
,
Hybrid Toolbox User's Guide
,
Matlab
, epub. http://cse.lab.imtlucca.it/~bemporad/hybrid/toolbox/
45.
Borrelli
,
F.
,
Bemporad
,
A.
, and
Morari
,
M.
,
2011
,
Predictive Control for Linear and Hybrid Systems
,
Cambridge University Press
,
Cambridge, UK
.
46.
Erickson
,
V. L.
,
Carreira-Perpiñán
,
M. Á.
, and
Cerpa
,
A. E.
,
2014
, “
Occupancy Modeling and Prediction for Building Energy Management
,”
ACM Trans. Sens. Networks
,
10
(
3),
p.
42
.10.1145/2594771
47.
Hoes
,
P.
,
Hensen
,
J. L. M.
,
Loomans
,
M. G. L. C.
,
de Vries
,
B.
, and
Bourgeois
,
D.
,
2009
, “
User Behavior in Whole Building Simulation
,”
Energy Build.
,
41
(
3
), pp.
295
302
.10.1016/j.enbuild.2008.09.008
48.
Wang
,
W.
,
Chen
,
J.
,
Huang
,
G.
, and
Lu
,
Y.
,
2017
, “
Energy Efficient HVAC Control for an IPS-Enabled Large Space in Commercial Buildings Through Dynamic Spatial Occupancy Distribution
,”
Appl. Energy
,
207
, pp.
305
323
.10.1016/j.apenergy.2017.06.060
49.
Shen
,
W.
,
Newsham
,
G.
, and
Gunay
,
B.
,
2017
, “
Leveraging Existing Occupancy-Related Data for Optimal Control of Commercial Office Buildings: A Review
,”
Adv. Eng. Inf.
,
33
, pp.
230
242
.10.1016/j.aei.2016.12.008
50.
Levine
,
S.
,
2015
,
Temperature Simulation in Residential Building by Use of Electrical Circuits
,
Washington University
, Systems Science and Engineering, St. Louis, MO.
51.
Wilson
,
M. B.
,
Luck
,
R.
, and
Mago
,
P. J.
,
2015
, “
A First-Order Study of Reduced Energy Consumption Via Increased Thermal Capacitance With Thermal Storage Management in a Micro-Building
,”
Energies
,
8
(
10
), pp.
12266
12282
.10.3390/en81012266
52.
Yang
,
S.
,
Wan
,
M. P.
,
Ng
,
B. F.
,
Zhang
,
T.
,
Babu
,
S.
,
Zhang
,
Z.
,
Chen
,
W.
, and
Dubey
,
S.
,
2018
, “
A State-Space Thermal Model Incorporating Humidity and Thermal Comfort for Model Predictive Control in Buildings
,”
Energy Build.
,
170
, pp.
25
39
.10.1016/j.enbuild.2018.03.082
53.
Bourdeau
,
M.
,
Zhai qiang
,
X.
,
Nefzaoui
,
E.
,
Guo
,
X.
, and
Chatellier
,
P.
,
2019
, “
Modeling and Forecasting Building Energy Consumption: A Review of Data-Driven Techniques
,”
Sustain. Cities Soc.
,
48
(
2019
), p.
101533
.10.1016/j.scs.2019.101533
54.
De
,
S.
,
Zhou
,
Y.
,
Larizgoitia Abad
,
I.
, and
Moessner
,
K.
,
2017
, “
Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey
,”
Appl. Sci.
,
7
(
10
), p.
1017
.10.3390/app7101017
55.
Baleta
,
J.
,
Mikulčić
,
H.
,
Klemeš
,
J. J.
,
Urbaniec
,
K.
, and
Duić
,
N.
,
2019
, “
Integration of Energy, Water and Environmental Systems for a Sustainable Development
,”
J. Clean. Prod.
,
215
, pp.
1424
1436
.10.1016/j.jclepro.2019.01.035
56.
Afram
,
A.
, and
Janabi-Sharifi
,
F.
,
2014
, “
Theory and Applications of HVAC Control Systems—A Review of Model Predictive Control (MPC)
,”
Build. Environ.
,
72
, pp.
343
355
.10.1016/j.buildenv.2013.11.016
57.
Kvanica
,
M.
, and
Herceg
,
M.
,
2019
,
“HYSDEL—Hybrid System DEscription Language,” STU Institute of Information Engineering, Automation, and Mathematics, Bratislava, Slovakia,
accessed Dec. 18,
2019
, https://people.ee.ethz.ch/~cohysys/hysdel/download/HYSDEL3_manual.pdf
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