Smart Cooling Controlled System Exploiting Photovoltaic Renewable Energy Systems

• Autorzy: Atieh A. , Al Asfar J. , Tawalbeh N. , Shaqour E. , Alsalhi I. , Istaiteh O.

Identyfikatory

DOI

10.12911/22998993/81241

• Języki publikacji: EN

Abstrakty

EN

A smart cooling system to control the ambient temperature of a premise in Amman, Jordan, is investigated and implemented. The premise holds 650 people and has 14 air conditioners with the cooling capacity ranging from 3 to 5 ton refrigerant (TR) each. The control of the cooling system includes implementing different electronics circuits that are used to sense the ambient temperature and humidity, count the number of people in the premise and then turn ON/OFF certain air conditioner(s). The data collected by different electronic circuits are fed wirelessly to a microcontroller, which decides which air conditioner will be turned ON/OFF, its location and its desired set cooling temperature. The cooling system is integrated with an on-grid solar photovoltaic energy system to minimize the operational cost of the overall cooling system.

Słowa kluczowe

EN

smart cooling system; solar system; energy management; cost analysis

• Wydawca: Polskie Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology
• Czasopismo: Journal of Ecological Engineering
• Rocznik: 2018
• Tom: Vol. 19, nr 2
• Strony: 39--44
• Opis fizyczny: Bibliogr. 10 poz., tab., rys.

Twórcy

autor

Atieh A.

• School of Engineering, The University of Jordan, Amman, Jordan

autor

Al Asfar J.

• School of Engineering, The University of Jordan, Amman, Jordan

autor

Tawalbeh N.

• School of Engineering, The University of Jordan, Amman, Jordan

autor

Shaqour E.

• School of Engineering, The University of Jordan, Amman, Jordan

autor

Alsalhi I.

• School of Engineering, The University of Jordan, Amman, Jordan

autor

Istaiteh O.

• School of Engineering, The University of Jordan, Amman, Jordan

Bibliografia

• 1. Ari S., Cosden I.A., Khalifa H.E., Dannenhoffer J.F., Wilcoxen P., and Isik C., 2005. Constrained fuzzy logic approximation for indoor comfort and energy optimization. In: Proc. IEEE Fuzzy Inf. Process. Soc. Annu. Meet., Jun., 500–504.
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• 4. Fasfous J. Al Asfar, Hamdan M.A., Al-Salaymeh A., Sakhrieh A., Al-hamamre Z., and Al-bawwab A., 2013. Potential of utilizing solar cooling in the University of Jordan. Energy Conversion and Management, vol. 65, 729–735.
• 5. Liang J. and Du R., 2005. Thermal comfort control based on neural network for HVAC application,” in Proc. IEEE Control Appl. Conf., Toronto, Canada, 819–824.
• 6. Mathews E.H., Botha C.P., Arndt D.C., and Malan A., 2001. HVAC control strategies to enhance comfort and minimise energy usage. Energy Buildings, 33(8), 853–863.
• 7. Nassif N., Kajl S., and Sabourin R., 2004. Evolutionary algorithms for multi-objective optimization in HVAC system control strategy. In: Proc. IEEE North Amer. Fuzzy Inf. Proc. Soc. Annu. Meet. (NAFIPS), Banff, Canada, Jun., vol. 1, 51–56.
• 8. National Solar Radiation Data Base, Typical Meteorological Year 2. Solar Radiation Research Laboratory (online: http://www.nrel.gov/midc/srrl_bms/).
• 9. Thomas A.G., Jahangiri P., Wu D., Cai C., Zhao H., Aliprantis D.C., Tesfatsion L., 2012. Intelligent Residential Air-Conditioning System With Smart-Grid Functionality. IEEE Transactions on Smart Grid, 3(4), 2240–2251.
• 10. Wang Z., Wang L., Dounis A.I., Yang R., 2012. Multi-agent control system with information fusion based comfort model for smart buildings. Applied Energy, vol. 99, 247–254.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).