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Experimental Study of Air Conditioning Control System for Building Energy Saving
Available online at www.sciencedirect.com
ScienceDirect Energy Procedia 61 (2014) 63 – 66
The 6th International Conference on Applied Energy – ICAE2014
Experimental study of air conditioning control system for building energy saving Henry Nasutiona*, K. Sumerub, Azhar Abdul Aziza, Mohd. Yusoff Senawib b
a Automotive Development Centre, Universiti Teknologi Malaysia, Skudai 81310 - Johor, Malaysia Department of Thermofluid, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Skudai – 81310, Malaysia
Abstract Reducing energy consumption and ensuring thermal comfort are two important considerations in designing an air conditioning (AC) system. An AC system, originally operated on an on/off control mechanism, was retrofitted to enable the application of the new controllers. This paper deals with independent control methods for the AC system based on the thermostat on/off control, digital on/off control, digital on/off control with a personal computer (DPC) and fuzzy logic control (FLC). Measurements were made at a time interval of one minute for set point temperatures of 22, 23 and 24oC. The room air temperature, energy consumption and energy saving were analyzed for all control methods. The main objective is to determine the amount of energy savings when digital on/off control, DPC and FLC are applied to the AC system. The experimental results showed that these controls saved energy consumption and improved indoor comfort significantly for a building AC system compared to a thermostat on/off control method.
© 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
© 2014 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection peer-review of under responsibility of ICAE Peer-reviewand/or under responsibility the Organizing Committee of ICAE2014
Keywords: On/off control, fuzzy logic control, air conditioning, central unit, energy saving
1. Introduction Air conditioning (AC) is increasingly being used in residential and commercial buildings which directly contribute to increased energy consumption. The challenge for the AC professionals is on how to best maintain the thermal comfort level of occupants and yet consume energy efficiently. However, in practice thermal comfort is not often realized because the capacity of installed AC system always exceeds
* Corresponding author. Tel.: +6-075-535-447; fax: +6-075-535-811. E-mail address: [email protected].
1876-6102 © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Peer-review under responsibility of the Organizing Committee of ICAE2014 doi:10.1016/j.egypro.2014.11.907
64
Henry Nasution et al. / Energy Procedia 61 (2014) 63 – 66
the actual load by 10-15% based on considerations for future development and the request of building owners. This over sizing leads to wasteful usage of energy [1]. Nomenclature C
cool
FT
fast
H
hot
N,NM,NO
normal
NE
negative
PO
positive
SL
slow
Energy consumption can be reduced by improving the performance of the compressor or the coefficient of performance (COP), which depends on the speed of the compressor. COP value will be larger at the lower compressor speed which results in low energy consumption. Increase in compressor speed results in increased energy consumption with low COP [2]. With inverter technology, compressor speed can be varied by changing the frequency, voltage and current of the compressor motor. This study aims to identify energy savings of an existing AC system by replacing the conventional thermostat control with on/off digital control and application of artificial intelligence (AI) control system. FLC is chosen as a model of AI control system. The study investigates the room temperature distribution, energy consumption and energy saving. 2. Fuzzy Logic Control Algorithm The major components of FLC are input and output variables, fuzzification, inference mechanism, fuzzy rule base and deffuzification [3]. The fuzzy input variables are the error (e) and the error difference ('e) with the universe of discourse for e and 'e is –2oC to +2oC. The universe of discourse for the output fuzzy variable ('Z) is 0 to 10 Vdc. The membership functions were chosen to be moderate overlap with – 2, –1, – 0.5, 0, 0.5, 1 and 2 distribution of input fuzzy subsets and 0, 2.5, 4, 5, 6, 7.5 and 10 distributions for output fuzzy subsets. Fig. 1 shows the diagrammatic representation of a fuzzy set corresponding. A Fuzzy logic rules corresponding to Fig. 1 is shown in Table 1.
Fig. 1. Triangular membership functions for inputs and output
Henry Nasution et al. / Energy Procedia 61 (2014) 63 – 66
Table 1. Fuzzy association map 'Z 'e
NE NO PO
H SL SL FT
e N SL SL NM
C SL SL SL
3. Experimental Setup The study focused on the centralized air-conditioning system. The AC system has two compressors (compressor A, 7 kW and compressor B, 5 kW). Fig. 2 shows the experimental setup. Five T type thermocouple sensors (points: T1, T2, T3, T4, and T5) and one humidity sensor (Rh2) were placed on the opposite walls in the laboratory. Thermocouple is used to transmit the temperature data to the dataacquisition system for the control action. The control signal output is supplied to the inverter, which modulates the electrical frequency supplied to the motor. The room setpoint temperatures during the experiments were 22, 23 and 24oC. In each experiment, the AC system responded to the actual cooling load that prevailed during the experimental period. The experimental work was conducted using thermostat control, digital on/off control, DPC, and FLC strategies to maintain the set-point temperature.
Fig. 2. Experimental setup
4. Results and Discussion Fig. 2 shows the indoor air temperature response at various temperature settings. The controller turns on the compressor motor when the room air temperature reaches the upper limit of temperature setting, and turns off at the lower limit temperature setting. In the fuzzy logic control experiment, initially the motor was set to run at the maximum speed and as time progressed the room air temperature decreased. Referring to the setpoint temperature, the controller minimized the error between the setpoint and the actual room temperature by changing the speed of the compressor motor. The motor speed drops abruptly as the room air temperature reaches the setpoint. The controller manipulates the motor speed so that the room air temperature is at or close to its setpoint temperature. Using digital on/off, DPC and FLC, the resulting room air temperatures were much closer to the setpoints when compared with the simple thermostat on/off control action.
65
66
Henry Nasution et al. / Energy Procedia 61 (2014) 63 – 66
o
Tset = 22 C
0
100 200 300 400 500 Time (minute)
27 26 25 24 23 22 21 20 19 18 17
Tset = 23oC
0
100 200 300 400 500 Time (minute)
RefTemp Dig On/Off
Room Temperature (oC)
27 26 25 24 23 22 21 20 19 18 17
Room Temperature (oC)
Room Temperature (oC)
Energy consumption at various temperature settings estimated are thermostat: 2191.70 kWh/month (22, 23 and 24oC), digital on/off: 1095.2 kWh/month (24oC); 1255 kWh/month (23oC); 1665.9 kWh/month (22oC), DPC: 1080.0 kWh/month (24oC); 1100.9 kWh/month (23oC); 1363.2 kWh/month (22oC), and FLC: 481.6 kWh/month (24 oC); 588.3 kWh/m (23oC); 1133 kWh/month (22oC). In comparison with simple thermostat control, the energy saving is estimated to be between 23 to 50% for digital on/off control, 37 to 51% for DPC and 48 to78% for FLC at temperature settings of 22, 23 and 24oC. 27 26 25 24 23 22 21 20 19 18 17
Tset = 24oC
0
100 200 300 400 500 Time (minute)
Therm DPC
Fig. 3. The indoor air temperature response at various temperature settings 5. Conclusions The research has shown that FLC gives the most energy savings and performs better than the other control systems investigated. This research shows that the use of a variable speed compressor and a suitable control strategy to enable better control of the room air temperature to be obtained with significant energy saving. Acknowledgements The research was supported financially by Universiti Teknologi Malaysia : Fundamental Research Grant Scheme (FRGS) No.78686, Ministry of Higher Education (MOHE) Malaysia. Their guidance and assistance are gratefully acknowledged. References [1] Yu PCH. A study of energy use for ventilation and air-conditioning system in Hong Kong. Hong Kong : The Hong Kong Politechnic University; 2001. [2] Nasution H. Energy analysis of an air conditioning system using PID and fuzzy logic control. Malaysia: Universiti Teknologi Malaysia; 2006. [3] Pasino KM, Yurkovich S. Fuzzy Control. United State of America: Addison Wesley; 1998.
Biography Henry Nasution obtained his PhD at the Universiti Teknologi Malaysia (UTM) in 2005 and then did a research officer from 2006 to 2008, visiting research from 2008 to 2009, senior lecturer and member of the Automotive Development Centre at UTM from 2010 to 2016.
ScienceDirect Energy Procedia 61 (2014) 63 – 66
The 6th International Conference on Applied Energy – ICAE2014
Experimental study of air conditioning control system for building energy saving Henry Nasutiona*, K. Sumerub, Azhar Abdul Aziza, Mohd. Yusoff Senawib b
a Automotive Development Centre, Universiti Teknologi Malaysia, Skudai 81310 - Johor, Malaysia Department of Thermofluid, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Skudai – 81310, Malaysia
Abstract Reducing energy consumption and ensuring thermal comfort are two important considerations in designing an air conditioning (AC) system. An AC system, originally operated on an on/off control mechanism, was retrofitted to enable the application of the new controllers. This paper deals with independent control methods for the AC system based on the thermostat on/off control, digital on/off control, digital on/off control with a personal computer (DPC) and fuzzy logic control (FLC). Measurements were made at a time interval of one minute for set point temperatures of 22, 23 and 24oC. The room air temperature, energy consumption and energy saving were analyzed for all control methods. The main objective is to determine the amount of energy savings when digital on/off control, DPC and FLC are applied to the AC system. The experimental results showed that these controls saved energy consumption and improved indoor comfort significantly for a building AC system compared to a thermostat on/off control method.
© 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
© 2014 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection peer-review of under responsibility of ICAE Peer-reviewand/or under responsibility the Organizing Committee of ICAE2014
Keywords: On/off control, fuzzy logic control, air conditioning, central unit, energy saving
1. Introduction Air conditioning (AC) is increasingly being used in residential and commercial buildings which directly contribute to increased energy consumption. The challenge for the AC professionals is on how to best maintain the thermal comfort level of occupants and yet consume energy efficiently. However, in practice thermal comfort is not often realized because the capacity of installed AC system always exceeds
* Corresponding author. Tel.: +6-075-535-447; fax: +6-075-535-811. E-mail address: [email protected].
1876-6102 © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Peer-review under responsibility of the Organizing Committee of ICAE2014 doi:10.1016/j.egypro.2014.11.907
64
Henry Nasution et al. / Energy Procedia 61 (2014) 63 – 66
the actual load by 10-15% based on considerations for future development and the request of building owners. This over sizing leads to wasteful usage of energy [1]. Nomenclature C
cool
FT
fast
H
hot
N,NM,NO
normal
NE
negative
PO
positive
SL
slow
Energy consumption can be reduced by improving the performance of the compressor or the coefficient of performance (COP), which depends on the speed of the compressor. COP value will be larger at the lower compressor speed which results in low energy consumption. Increase in compressor speed results in increased energy consumption with low COP [2]. With inverter technology, compressor speed can be varied by changing the frequency, voltage and current of the compressor motor. This study aims to identify energy savings of an existing AC system by replacing the conventional thermostat control with on/off digital control and application of artificial intelligence (AI) control system. FLC is chosen as a model of AI control system. The study investigates the room temperature distribution, energy consumption and energy saving. 2. Fuzzy Logic Control Algorithm The major components of FLC are input and output variables, fuzzification, inference mechanism, fuzzy rule base and deffuzification [3]. The fuzzy input variables are the error (e) and the error difference ('e) with the universe of discourse for e and 'e is –2oC to +2oC. The universe of discourse for the output fuzzy variable ('Z) is 0 to 10 Vdc. The membership functions were chosen to be moderate overlap with – 2, –1, – 0.5, 0, 0.5, 1 and 2 distribution of input fuzzy subsets and 0, 2.5, 4, 5, 6, 7.5 and 10 distributions for output fuzzy subsets. Fig. 1 shows the diagrammatic representation of a fuzzy set corresponding. A Fuzzy logic rules corresponding to Fig. 1 is shown in Table 1.
Fig. 1. Triangular membership functions for inputs and output
Henry Nasution et al. / Energy Procedia 61 (2014) 63 – 66
Table 1. Fuzzy association map 'Z 'e
NE NO PO
H SL SL FT
e N SL SL NM
C SL SL SL
3. Experimental Setup The study focused on the centralized air-conditioning system. The AC system has two compressors (compressor A, 7 kW and compressor B, 5 kW). Fig. 2 shows the experimental setup. Five T type thermocouple sensors (points: T1, T2, T3, T4, and T5) and one humidity sensor (Rh2) were placed on the opposite walls in the laboratory. Thermocouple is used to transmit the temperature data to the dataacquisition system for the control action. The control signal output is supplied to the inverter, which modulates the electrical frequency supplied to the motor. The room setpoint temperatures during the experiments were 22, 23 and 24oC. In each experiment, the AC system responded to the actual cooling load that prevailed during the experimental period. The experimental work was conducted using thermostat control, digital on/off control, DPC, and FLC strategies to maintain the set-point temperature.
Fig. 2. Experimental setup
4. Results and Discussion Fig. 2 shows the indoor air temperature response at various temperature settings. The controller turns on the compressor motor when the room air temperature reaches the upper limit of temperature setting, and turns off at the lower limit temperature setting. In the fuzzy logic control experiment, initially the motor was set to run at the maximum speed and as time progressed the room air temperature decreased. Referring to the setpoint temperature, the controller minimized the error between the setpoint and the actual room temperature by changing the speed of the compressor motor. The motor speed drops abruptly as the room air temperature reaches the setpoint. The controller manipulates the motor speed so that the room air temperature is at or close to its setpoint temperature. Using digital on/off, DPC and FLC, the resulting room air temperatures were much closer to the setpoints when compared with the simple thermostat on/off control action.
65
66
Henry Nasution et al. / Energy Procedia 61 (2014) 63 – 66
o
Tset = 22 C
0
100 200 300 400 500 Time (minute)
27 26 25 24 23 22 21 20 19 18 17
Tset = 23oC
0
100 200 300 400 500 Time (minute)
RefTemp Dig On/Off
Room Temperature (oC)
27 26 25 24 23 22 21 20 19 18 17
Room Temperature (oC)
Room Temperature (oC)
Energy consumption at various temperature settings estimated are thermostat: 2191.70 kWh/month (22, 23 and 24oC), digital on/off: 1095.2 kWh/month (24oC); 1255 kWh/month (23oC); 1665.9 kWh/month (22oC), DPC: 1080.0 kWh/month (24oC); 1100.9 kWh/month (23oC); 1363.2 kWh/month (22oC), and FLC: 481.6 kWh/month (24 oC); 588.3 kWh/m (23oC); 1133 kWh/month (22oC). In comparison with simple thermostat control, the energy saving is estimated to be between 23 to 50% for digital on/off control, 37 to 51% for DPC and 48 to78% for FLC at temperature settings of 22, 23 and 24oC. 27 26 25 24 23 22 21 20 19 18 17
Tset = 24oC
0
100 200 300 400 500 Time (minute)
Therm DPC
Fig. 3. The indoor air temperature response at various temperature settings 5. Conclusions The research has shown that FLC gives the most energy savings and performs better than the other control systems investigated. This research shows that the use of a variable speed compressor and a suitable control strategy to enable better control of the room air temperature to be obtained with significant energy saving. Acknowledgements The research was supported financially by Universiti Teknologi Malaysia : Fundamental Research Grant Scheme (FRGS) No.78686, Ministry of Higher Education (MOHE) Malaysia. Their guidance and assistance are gratefully acknowledged. References [1] Yu PCH. A study of energy use for ventilation and air-conditioning system in Hong Kong. Hong Kong : The Hong Kong Politechnic University; 2001. [2] Nasution H. Energy analysis of an air conditioning system using PID and fuzzy logic control. Malaysia: Universiti Teknologi Malaysia; 2006. [3] Pasino KM, Yurkovich S. Fuzzy Control. United State of America: Addison Wesley; 1998.
Biography Henry Nasution obtained his PhD at the Universiti Teknologi Malaysia (UTM) in 2005 and then did a research officer from 2006 to 2008, visiting research from 2008 to 2009, senior lecturer and member of the Automotive Development Centre at UTM from 2010 to 2016.