A new approach to investigate the energy performance of a household refrigerator-freezer

Saidur, Rahman and Masjuki, Haji Hassan and Jamaluddin, M.Y. (2006) A new approach to investigate the energy performance of a household refrigerator-freezer. International Energy Journal, 7 (1). pp. 13-23. ISSN 1513-718X,

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There are number of methods (i.e. engineering, regression) and computer tools (i.e. DOE-2, BLAST, HOT2000, ENERGY-10) for the modeling and forecasting of energy. Recently, a new approach artificial neural network has been widely used for load forecasting, solar energy, heating, ventilating, refrigeration, building energy analysis and so on in the field of energy as its (i.e. NN) prediction performance is better than other approaches in non-linear modeling analysis as has been found in literatures. A Neural Network (NN) also commonly referred to as an Artificial Neural Network, is an information-processing model inspired by the way the densely interconnected, parallel structure of the brain processes information. In this paper, experiments were conducted on a refrigerator to investigate the energy performance by varying the parameters (i.e. room temperature, door opening, internal cabinet temperatures, relative humidity and so on) that influence its energy consumption. Finally, experimental data were used to investigate refrigerators' energy prediction performance using NN approach. Statistical analyses in terms of fraction of variance R 2, Coefficient of variation (COV), RMS are calculated to judge the performance of NN model.

Item Type: Article
Additional Information: Export Date: 6 December 2012 Source: Scopus CODEN: IEJNA Language of Original Document: English Correspondence Address: Saidur, R.; Department of Mechanical Engineering, University of Malaya, 50603 Kuala Lumpu, Malaysia; email: saidur@um.edu.my References: (2003) Statistics of Electricity in Malaysia, , Energy commission Malaysia, Energy Commission of Malaysia, Kuala Lumpur; Masjuki, H.H., Mahlia, T.M.I., Saidur, R., Choudhury, I.A., Noor Leha, A.R., Projecting electricity savings from implementing minimum energy efficiency standards for household refrigerator in Malaysia (2003) Energy-the Int. Journal, 28 (7), pp. 751-754. , 2003; Study on residential customer load profile (2001) Demand Side Management, Final Report, , DSM, Petaling Jaya, Malaysia; Kalogirou Soteris, A., Applications of artificial neural networks for energy systems (2000) Applied Energy, 67, pp. 17-35; Sozen, A., Arcaklioglu, E., Ozalp, M., Performance analysis of ejector absorption heat pump using ozone safe fluid couple through artificial neural networks (2004) Energy Conversion and Management, 45, pp. 2233-2253; Sozen, A., Arcaklioglu, E., Ozkaymak, M., Turkey's net energy consumption (2005) Applied Energy, , To be published; Sozen, A., Arcaklioglub, E., Ozalpa, M., Kanit, E.G., Use of artificial neural networks for mapping of solar potential in Turkey (2004) Applied Energy, 77, pp. 273-286; Mohandes, M., Rehman, S., Halawani, T.O., Estimation of global solar radiation using artificial neural networks (1998) Renewable Energy, 14, pp. 179-184; Bechtler, H., Browne, M.W., Bansal, P.K., Kecman, V., New approach to dynamic modeling of vapor,compression liquid chillers: Artificial neural networks (2001) Appl Thermal Eng, 21, pp. 941-953; AlFuhaid, A.S., El-Sayed, M.A., Mahmoud, M.S., Cascaded artificial neural networks for short term load forecasting (1997) IEEE Transactions on Power Systems, 12, pp. 1524-1529; Sozen, A., Arcaklioglu, E., Solar potential in Turkey (2005) Applied Energy, 80, pp. 35-45; Kreider, J.F., Wang, X.A., Artificial neural networks demonstrations for automated generation of energy use predictors for commercial buildings (1992) ASHRAE Transactions, 97 (1), pp. 775-779; Beccali, M., Cellura, M., Lo Brano, V., Marvuglia, A., Forecasting daily urban electric load profiles using artificial neural networks (2004) Energy Conversion and Management, 45, pp. 2879-2900; Aydinalp, M., Ugursal, V.I., Fung, A.S., Modeling of the space and domestic hot water heating energy consumption in the residential sector using neural networks (2004) Applied Energy, 79, pp. 159-178; Aydinalp, M., Ugursal, V.I., Fung, A.S., Modeling of the appliance, lighting, and space cooling energy consumptions in the residential sector using neural networks (2002) Applied Energy, 71, pp. 87-110; Stuttgart neural network simualtor (SNNS) (1998) User Manual Version 4.2, , Stuttgart, University of Stuttgart, Germany; Household refrigerators and freezers (1988) ASHRAE Handbook (Equipment), , Atlanta: ASHRAE; Meier, A., Refrigerator energy use in the laboratory and in the field (1995) Energy and Building, 22, p. 23343; Alissi, M.S., (1987) The Effect of Ambient Temperature, Ambient Humidity and Door Openings on Household Refrigerator Energy Consumption, , MSME thesis, Purdue University, Indiana; Grimes, J.G., William, P.E.M., Shomaker, B.L., Effect of usage conditions on household refrigerator freezer and freezer energy consumption (1977) ASHRAE Transaction, 83 (1), p. 81828; Meier, A.K., Do refrigerator thermostat setups save energy? (1994) Home Energy, 11 (3), p. 11; Parker, D.S., Stedman, T.C., Case study and analysis: Measured savings of refrigerator replacement (1992) Proc. Conf. of ACEEE Summer Study on Energy Efficiency in Buildings, pp. 199-212. , Mary AP, editor. Washington (DC): American Council for Energy efficiency Economy; Yunus, A.C., Michael, A.B., (1998) Thermodynamics: An Engineering Approach. 3rd Ed., pp. 125-126. , Boston: McGrawHill; (1993) Study on Energy Efficiency Standards for Domestic Refrigeration Appliances, , Group for Efficient Appliances. Report, GEA; Refrigeration system and application (1990) ASHRAE Handbook, , Atlanta: ASHRAE
Uncontrolled Keywords: Door opening; Energy; Neural networks; Refrigerator-freezers; Statistical analysis; Atmospheric humidity; Computer simulation; Energy management; Freezing; Statistical methods; Coefficient of variation; Internal cabinet temperatures; Refrigerators.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 09 Jul 2013 02:12
Last Modified: 18 Oct 2018 05:25
URI: http://eprints.um.edu.my/id/eprint/6894

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