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Fuzzy logic based energy saving technique for a central air conditioning system

Author

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  • Shahnawaz Ahmed, S.
  • Shah Majid, Md.
  • Novia, Hendri
  • Abd Rahman, Hasimah

Abstract

In this paper a scheme has been proposed to maintain the temperature and the humidity, in each of the rooms served by a central Air Conditioner (AC) unit, close to the targeted values, and reduce the electrical energy intake of the AC compressor. The upper limits of the comfort zone, typically marked at a temperature of 25°C and a relative humidity of 70%, are used as the targets. It should be noted that a conventional AC system controls humidity in its own way without giving the users any scope for changing the set point for the targeted humidity unlike the scope it offers to change the set point for the targeted temperature through a thermostat. But in this work this limitation has been taken into cognizance and overcome to a great extent using fuzzy logic to represent the intricate influences of temperature on the humidity of the space being cooled and correct the thermostat setting. In the developed scheme, the sensor captured temperature and humidity readings for each room are compared against the targets at the selected intervals of time, and the corresponding differences are fuzzified. These differences are used to decide the fuzzy qualifier, which is decoded into a crisp value that is the change required in the setting of the thermostat of the AC. As a result, each room will maintain a temperature near 25°C and a relative humidity near 70% while the compressor will remain off for an appreciable period leading to a saving of energy. Though a thermostat with programmable setting for an AC unit dedicated to a single room has been reported in the literature, the same for a central AC unit that serves more than one room appears to have not yet been presented. The advantages of the scheme proposed for programming a thermostat under central air conditioning system are that it (i) requires for each room only a pair of input data i.e. the sensor captured temperature and humidity readings for each room, (ii) controls humidity indirectly and (iii) leads to a saving in energy consumption while maintaining a comfortable level of cooling in each of the rooms though their occupancy, size and the thermal conditions are different from one another.

Suggested Citation

  • Shahnawaz Ahmed, S. & Shah Majid, Md. & Novia, Hendri & Abd Rahman, Hasimah, 2007. "Fuzzy logic based energy saving technique for a central air conditioning system," Energy, Elsevier, vol. 32(7), pages 1222-1234.
    • Handle: RePEc:eee:energy:v:32:y:2007:i:7:p:1222-1234
    DOI: 10.1016/j.energy.2006.07.025
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    References listed on IDEAS

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    1. Dounis, A. I. & Manolakis, D. E., 2001. "Design of a fuzzy system for living space thermal-comfort regulation," Applied Energy, Elsevier, vol. 69(2), pages 119-144, June.
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    2. Satish Suresh Tanavade & Ganesh Nagraj Patil & C. V. Sudhir & A. M. Saravanan, 2023. "Strategic Energy Management and Carbon Footprint Reduction in University Campuses: A Comprehensive Review," International Journal of Energy Economics and Policy, Econjournals, vol. 13(6), pages 15-27, November.
    3. Spandagos, Constantine & Ng, Tze Ling, 2018. "Fuzzy model of residential energy decision-making considering behavioral economic concepts," Applied Energy, Elsevier, vol. 213(C), pages 611-625.
    4. Parameshwaran, R. & Kalaiselvam, S. & Harikrishnan, S. & Elayaperumal, A., 2012. "Sustainable thermal energy storage technologies for buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 2394-2433.
    5. Khan, Muhammad Waqas & Choudhry, Mohammad Ahmad & Zeeshan, Muhammad & Ali, Ahsan, 2015. "Adaptive fuzzy multivariable controller design based on genetic algorithm for an air handling unit," Energy, Elsevier, vol. 81(C), pages 477-488.
    6. Ganesh Nagraj Patil & Satish Suresh Tanavade, 2024. "Eco-Friendly Energy Efficient Classrooms and Sustainable Campus Strategies: A Case Study on Energy Management and Carbon Footprint Reduction," International Journal of Energy Economics and Policy, Econjournals, vol. 14(3), pages 188-197, May.
    7. Karunakaran, R. & Iniyan, S. & Goic, Ranko, 2010. "Energy efficient fuzzy based combined variable refrigerant volume and variable air volume air conditioning system for buildings," Applied Energy, Elsevier, vol. 87(4), pages 1158-1175, April.
    8. Kamal Rsetam & Mohammad Al-Rawi & Ahmed M. Al-Jumaily & Zhenwei Cao, 2023. "Finite Time Disturbance Observer Based on Air Conditioning System Control Scheme," Energies, MDPI, vol. 16(14), pages 1-28, July.
    9. Gomes, A. & Antunes, C. Henggeler & Martinho, J., 2013. "A physically-based model for simulating inverter type air conditioners/heat pumps," Energy, Elsevier, vol. 50(C), pages 110-119.

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