Energy efficiency of small buildings with smart cooling system in the summer
Yazdan DANESHVAR1, Majid SABZEHPARVAR2(), Seyed Amir Hossein HASHEMI1
1. Department of Civil Engineering, Qazvin Branch, Islamic Azad University, Qazvin 314199-15195, Iran 2. Department of Industrial Engineering, Collage of Engineering, Karaj Branch, Islamic Azad University, Karaj 31499-68111, Iran
In this paper, a novel cooling control strategy as part of the smart energy system that can balance thermal comfort against building energy consumption by using the sensing and machine programming technology was investigated. For this goal, a general form of a building was coupled by the smart cooling system (SCS) and the consumption of energy with thermal comfort cooling of persons simulated by using the EnergyPlus software and compared with similar buildings without SCS. At the beginning of the research, using the data from a survey in a randomly selected group of hundreds and by analyzing and verifying the results of the specific relationship between the different groups of people in the statistical society, the body mass index (BMI) and their thermal comfort temperature were obtained, and the sample building was modeled using the EnergyPlus software. The result show that if an intelligent ventilation system that can calculate the thermal comfort temperature was used in accordance with the BMI of persons, it can save up to 35% of the cooling load of the building yearly.
. [J]. Frontiers in Energy, 2022, 16(4): 651-660.
Yazdan DANESHVAR, Majid SABZEHPARVAR, Seyed Amir Hossein HASHEMI. Energy efficiency of small buildings with smart cooling system in the summer. Front. Energy, 2022, 16(4): 651-660.
Double clear pane (12 mm air space, 5.7 mm for both inner and outer panes)
0.771
0.07
0.07
0.884
0.08
0.08
0.84
0.84
Tab.3
Fig.3
Fig.4
Fig.5
Fig.6
Category
BMI range
Percent/%
A
BMI≤18.5
31
B
18.5<BMI≤24.9
23
C
24.9<BMI≤29.9
29
D
29.9<BMI
17
Tab.4
Fig.7
Physical level
Amount of energy/(W·m–2)
Light
Mean
Heavy
Body
95–155
155–230
230–330
Tab.5
Name of category
Thermal comfort temperatures of persons
g1
TH, TH, TH
g2
TC, TC, TC
g3
TM, TM, TM
g4
TH, TH, TC
g5
TH, TH, TM
g6
TC, TC, TH
g7
TC, TC, TM
g8
TM, TM, TC
g9
TM, TM, TH
g10
TC, TM, TH
Tab.6
Fig.8
Name of category
Thermal comfort temperatures of persons
g2
TC, TC, TC
g21
TC, TC, TC+
g22
TC, TC, TC++
g23
TC, TC+, TC+
g24
TC, TC+, TC++
Tab.7
Name of category
Thermal comfort temperatures of persons
g8
TM, TM, TC
g81
TM, TM, TC+
g82
TM, TM+, TC
g83
TM, TM, TC++
g84
TM, TM++ , TC
g85
TM, TM+, TC+
g86
TM+, TM+, TC
g87
TM, TM+, TC++
g88
TM, TM++ , TC+
g89
TM+, TM++ , TC
Tab.8
Name of category
Thermal comfort temperatures of persons
g10
TC, TM, TH
g101
TC, TM, TH+
g103
TC, TM+, TH
g104
TC+, TM, TH
g105
TC, TM, TH++
g106
TC, TM++ , TH
g107
TC++ , TM, TH
g108
TC, TM+, TH+
g109
TC+, TM, TH+
g1010
TC+, TM+, TH
g1011
TC, TM+, TH++
g1012
TC, TM++ , TH+
g1013
TC+, TM, TH++
g1014
TC++ , TM, TH+
g1015
TC++ , TM+, TH
g1016
TC+, TM++ , TH
Tab.9
Fig.9
Fig.10
Fig.11
A
Name of a group based on the BMI
B
Name of a group based on the BMI
C
Name of a group based on the BMI
D
Name of a group based on the BMI
BMI
Body mass index/(kg·m–2)
COP
Coefficient of performance
e
Cooling load, (a ton of refrigeration)
g
Name of a group or sun group based on thermal comfort temperature
N
Numbers of parameter
T
Temperature/°C
TC
Low thermal comfort temperature range/°C
TH
High thermal comfort temperature range/°C
TM
Moderate thermal comfort temperature range/°C
AVE
Average
i
Studied parameter
n
Cooling load based on lower thermal comfort temperature
o
Cooling load based on mean thermal comfort temperature
+
Moderate physical activity
++
High physical activity
CPP
Critical-peak pricing
EEIs
Energy efficiency indicators
HVAC
Heating, ventilation, and air conditioning
RTP
Real-time pricing
SCS
Smart cooling system
ToUP
Time-of-use pricing
TMY
Typical meteorological year
WSN
Wireless sensor network
1
J Paradiso, P Dutta, H Gellersen, E Schooler. Smart energy systems. IEEE Pervasive Computing, 2011, 10(1): 11–12 https://doi.org/10.1109/MPRV.2011.4
2
X Fang, S Misra, G Xue, D Yang. Smart grid—the new and improved power grid: a survey. IEEE Communications Surveys and Tutorials, 2012, 14(4): 944–980 https://doi.org/10.1109/SURV.2011.101911.00087
3
G Lu, D De, W Z Song. Smart grid lab: a laboratory-based smart grid test bed. In: 2010 1st IEEE International Conference on Smart Grid Communications, Gaithersburg, MD, USA, 2010, 143–148
4
A H Mohsenian-Rad, V W Wong, J Jatskevich, R Schober, A Leon-Garcia. Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Transactions on Smart Grid, 2010, 1(3): 320–331 https://doi.org/10.1109/TSG.2010.2089069
5
Z Zhu, S Lambotharan, W H Chin, Z. FanOverview of demand management in smart grid and enabling wireless communication technologies. IEEE Wireless Communications, 2012, 19(3): 48–56 https://doi.org/10.1109/MWC.2012.6231159
6
H T Nguyen, D Nguyen, L B Le. Home energy management with generic thermal dynamics and user temperature preference . In: 2013 IEEE International Conference on Smart Grid Communications (Smart Grid Comm), Vancouver, BC, Canada, 2013, 552–557
7
Z Zhu, J Tang, S Lambotharan, W H Chin, Z Fan. An integer linear programming based optimization for home demand-side management in smart grid. In: 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), Washington, DC, USA, 2012, 1–5
8
A H Mohsenian-Rad, A Leon-Garcia. Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Transactions on Smart Grid, 2010, 1(2): 120–133 https://doi.org/10.1109/TSG.2010.2055903
9
M S Pan, L W Yeh, Y A Chen, Y H Lin, Y C Tseng. A WSN-based intelligent light control system considering user activities and profiles. IEEE Sensors Journal, 2008, 8(10): 1710–1721 https://doi.org/10.1109/JSEN.2008.2004294
10
C Gouy-Pailler, H Najmeddine, A Mouraud, F Suard, C Spitz, A Jay, P Maréchal. Distance and similarity measures for sensors selection in heavily instrumented buildings: application to the INCAS platform. In: Proceedings of the CIB W78–W102, Sophia Antipolis, France, 2011, 26–28
11
J Lu, T Sookoor, V Srinivasan, G Gao, B Holben, J Stankovic, E Field, K. Whitehouse The smart thermostat: using occupancy sensors to save energy in homes. In: Proceedings of the 8th ACM conference on embedded networked sensor systems, Zurich, Switzerland, 2010, 211–224
12
M Weiss, T Staake, F Mattern, E Fleisch. PowerPedia: changing energy usage with the help of a community-based smartphone application. Personal and Ubiquitous Computing, 2012, 16(6): 655–664 https://doi.org/10.1007/s00779-011-0432-y
13
A Ebrahimpour, M Maerefat. Application of advanced glazing and overhangs in residential buildings. Energy Conversion and Management, 2011, 52(1): 212–219 https://doi.org/10.1016/j.enconman.2010.06.061
14
G Singh, R Das. A novel design of triple-hybrid absorption radiant building cooling system with desiccant dehumidification. Journal of Energy Resources Technology, 2019, 141(7): 072002 https://doi.org/10.1115/1.4042239
15
G Singh, R Das. Energy saving potential of a combined solar and natural gas-assisted vapor absorption building cooling system. Journal of Solar Energy Engineering, 2019, 141(1): 011016 https://doi.org/10.1115/1.4041104
16
C Obasi, N Chukwu, L Ogbewey, Asogwa C S. Design and implementation of automated healthcare software system for outpatient health maintenance. Computer Engineering and Intelligent Systems, 2016
17
G Halhoul Merabet, M Essaaidi, D Benhaddou. A dynamic model for human thermal comfort for smart building applications. Proceedings of the Institution of Mechanical Engineers. Part I, Journal of Systems and Control Engineering, 2020, 234(4): 472–483 https://doi.org/10.1177/0959651819865795
18
A Somelo, G H Havaei, , M Behabadi, Hamidi A. Iranian standards for buildings, ministry office of buildings and constructions. 2001, available at website of sabanaft.com
19
A L Chan, T T Chow, S K Fong, J Z Lin. Generation of a typical meteorological year for Hong Kong. Energy Conversion and Management, 2006, 47(1): 87–96 https://doi.org/10.1016/j.enconman.2005.02.010
20
L O Degelman. A statistically-based hourly weather data generator for driving energy simulation and equipment design software for buildings. Proceedings of Building Simulation, 1991, 91(1): 592–599
21
Meteonorm software, documentation, Version 5.102. Iran, Tehran, 2016
22
A Ebrahimpour, M Maerefat. A method for generation of typical meteorological year. Energy Conversion and Management, 2010, 51(3): 410–417 https://doi.org/10.1016/j.enconman.2009.10.002
23
D Sabzi, P Haseli, M Jafarian, G Karimi, M Taheri. Investigation of cooling load reduction in buildings by passive cooling options applied on roof. Energy and Buildings, 2015, 109(1): 135–142 https://doi.org/10.1016/j.enbuild.2015.09.042
24
H Zhang, E Arens, W Pasut. Air temperature thresholds for indoor comfort and perceived air quality. Building Research and Information, 2011, 39(2): 134–144 https://doi.org/10.1080/09613218.2011.552703
25
V Candas. Techniques of the engineer treated energy genie. Scientific Research National Center, Strasbourg, France, 2000, 1(9): 9–85 (in French)
