A Fuzzy Logic-Based Automation toward Intelligent Air Conditioning Systems
Corresponding Author(s) : Gunawan Dewantoro
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 5, No. 4, November 2020
Abstract
Most of the energy used in residential buildings originates from air conditioners. Meanwhile, air conditioner manufacturers are addressing this issue by the production of efficient air conditioners. However, the convertible frequency air conditioners are expensive, up to 60% higher than the fixed frequency control air conditioners. Besides the human behavior in determining the temperature, setpoint plays an important role regardless of the air conditioners technology used. This study incorporated intelligence in setting up the temperature by means of specially designed remote control. The Tsukamoto fuzzy reasoning was utilized as a decision making system with two inputs, namely the outdoor temperature and the number of occupants. The device used DHT22 as the temperature sensor and HC-SR04 to detect incoming and outgoing occupants. Furthermore, the fuzzy inference system generated infrared signal associated with the temperature setpoint. This signal was received by the air conditioner receiver to adjust the temperature setpoint accordingly. The result of this study showed that the fuzzy inference system determines the temperature setpoint appropriately under variations of surrounding temperature and the number of occupants. The proposed approach yielded a satisfactory perception of thermal comfort and also a promising approach to energy conservation.
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- L. Pérez-Lombard, J. Ortiz, and C. Pout, “A review on buildings energy consumption information,” Energy Build., vol. 40, no. 3, pp. 394–398, 2008, doi: 10.1016/j.enbuild.2007.03.007.
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References
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A. Arteconi, A. Mugnini, and F. Polonara, “Energy flexible buildings: A methodology for rating the flexibility performance of buildings with electric heating and cooling systems,” Appl. Energy, vol. 251, no. May, p. 113387, 2019, doi: 10.1016/j.apenergy.2019.113387.
P. H. Shaikh, N. B. M. Nor, P. Nallagownden, I. Elamvazuthi, and T. Ibrahim, “A review on optimized control systems for building energy and comfort management of smart sustainable buildings,” Renew. Sustain. Energy Rev., vol. 34, pp. 409–429, 2014, doi: 10.1016/j.rser.2014.03.027.
A. Elmoudi, O. Asad, M. Erol-Kantarci, and H. T. Mouftah, “Energy Consumption Control of an Air Conditioner Using Web Services,” Smart Grid Renew. Energy, vol. 02, no. 03, pp. 255–260, 2011, doi: 10.4236/sgre.2011.23028.
M. Mowad, A. Fathy, and A. Hafez, “Smart home automated control system using android application and microcontroller,” Int. J. Sci. Eng. Res., vol. 5, no. 5, pp. 935–939, 2014.
G. Graditi et al., “Innovative control logics for a rational utilization of electric loads and air-conditioning systems in a residential building,” Energy Build., vol. 102, pp. 1–17, 2015, doi: 10.1016/j.enbuild.2015.05.027.
W. W. Shein, Y. Tan, and A. O. Lim, “PID controller for temperature control with multiple actuators in cyber-physical home system,” in Proceedings of the 2012 15th International Conference on Network-Based Information Systems, NBIS 2012, 2012, pp. 423–428, doi: 10.1109/NBiS.2012.118.
C. C. Cheng and D. Lee, “Smart sensors enable smart air conditioning control,” Sensors, vol. 14, no. 6, pp. 11179–11203, 2014, doi: 10.3390/s140611179.
H. Khayyam, “Adaptive intelligent control of vehicle air conditioning system,” Appl. Therm. Eng., vol. 51, no. 1–2, pp. 1154–1161, 2013, doi: 10.1016/j.applthermaleng.2012.10.028.
Husein, M. Budiman, and M. Djamal, “Duty cycle control on compressor of split air conditioners using internet of things embedded in fuzzy-PID,” Int. J. Electr. Eng. Informatics, vol. 11, no. 1, pp. 112–124, 2019, doi: 10.15676/ijeei.2019.11.1.7.
S. K. Dash and G. Mohanty, “Intelligent Air Conditioning System using Fuzzy Logic,” Int. J. Sci. Eng. Res., vol. 3, no. 12, pp. 1–6, 2012.
Y. Hong, J. Lin, C. Wu, and C. Chuang, “Multi-Objective Air-Conditioning Control Considering Fuzzy Parameters Using Immune Clonal Selection Programming,” IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 1603–1610, 2012, doi: 10.1109/TSG.2012.2210059.
T. Fukazawa, Y. Iwata, J. Morikawa, and C. Ninagawa, “Stabilization of neural network by combination with AR model in FastADR control of building air-conditioner facilities,” IEEJ Trans. Electr. Electron. Eng., vol. 11, no. 1, pp. 124–125, 2016, doi: 10.1002/tee.22196.
N. Yamtraipat, J. Khedari, J. Hirunlabh, and J. Kunchornrat, “Assessment of Thailand indoor set-point impact on energy consumption and environment,” Energy Policy, vol. 34, no. 7, pp. 765–770, 2006, doi: 10.1016/j.enpol.2004.07.009.
T. Hoyt, E. Arens, and H. Zhang, “Extending air temperature setpoints: Simulated energy savings and design considerations for new and retrofit buildings,” Build. Environ., vol. 88, pp. 89–96, 2015, doi: 10.1016/j.buildenv.2014.09.010.
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N. H. A. Majid, N. Takagi, S. Hokoi, S. N. N. Ekasiwi, and T. Uno, “Field survey of air conditioner temperature settings in a hot, dry climate (Oman),” HVAC R Res., vol. 20, no. 7, pp. 751–759, 2014, doi: 10.1080/10789669.2014.953845.
H. B. Gunay, W. Shen, G. Newsham, and A. Ashouri, “Modelling and analysis of unsolicited temperature setpoint change requests in office buildings,” Build. Environ., vol. 133, pp. 203–212, 2018, doi: 10.1016/j.buildenv.2018.02.025.
T. A. Nguyen and M. Aiello, “Energy intelligent buildings based on user activity: A survey,” Energy Build., vol. 56, pp. 244–257, 2013, doi: 10.1016/j.enbuild.2012.09.005.
H. Hagras, I. Packham, Y. Vanderstockt, N. McNulty, A. Vadher, and F. Doctor, “An intelligent agent based approach for energy management in commercial buildings,” in IEEE International Conference on Fuzzy Systems, 2008, pp. 156–162, doi: 10.1109/FUZZY.2008.4630359.
G. S. Song, J. H. Lim, and T. K. Ahn, “Air conditioner operation behaviour based on students’ skin temperature in a classroom,” Appl. Ergon., vol. 43, no. 1, pp. 211–216, 2012, doi: 10.1016/j.apergo.2011.05.009.
J. Yao, “Modelling and simulating occupant behaviour on air conditioning in residential buildings,” Energy Build., vol. 175, pp. 1–10, 2018, doi: 10.1016/j.enbuild.2018.07.013.
J. Kim, Y. Zhou, S. Schiavon, P. Raftery, and G. Brager, “Personal comfort models: Predicting individuals’ thermal preference using occupant heating and cooling behavior and machine learning,” Build. Environ., vol. 129, pp. 96–106, 2018, doi: 10.1016/j.buildenv.2017.12.011.
Z. Gou, S. Y. S. Lau, and P. Lin, “Understanding domestic air-conditioning use behaviours: Disciplined body and frugal life,” Habitat Int., vol. 60, pp. 50–57, 2017, doi: 10.1016/j.habitatint.2016.12.009.
A. Silva, N. Mendes, R. Vilain, M. Pereira, and K. C. Mendonça, “On the development of a thermal comfort control for classrooms conditioned by split-type systems,” in ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE), 2019, vol. 8, no. May 2020, pp. 2–7, doi: 10.1115/IMECE2019-11426.
“https://www.accuweather.com/en/id/salatiga/202819/june-weather/202819?year=2019,” 2019.