Modeling of end-use energy consumption in the residential sector: A review of modeling techniques
There is a growing interest in reducing energy consumption and the associated greenhouse gas emissions in every sector of the economy. The residential sector is a substantial consumer of energy in every country, and therefore a focus for energy consumption efforts. Since the energy consumption characteristics of the residential sector are complex and inter-related, comprehensive models are needed to assess the technoeconomic impacts of adopting energy efficiency and renewable energy technologies suitable for residential applications. The aim of this paper is to provide an up-to-date review of the various modeling techniques used for modeling residential sector energy consumption. Two distinct approaches are identified: top-down and bottom-up. The top-down approach treats the residential sector as an energy sink and is not concerned with individual end-uses. It utilizes historic aggregate energy values and regresses the energy consumption of the housing stock as a function of top-level variables such as macroeconomic indicators (e.g. gross domestic product, unemployment, and inflation), energy price, and general climate. The bottom-up approach extrapolates the estimated energy consumption of a representative set of individual houses to regional and national levels, and consists of two distinct methodologies: the statistical method and the engineering method. Each technique relies on different levels of input information, different calculation or simulation techniques, and provides results with different applicability. A critical review of each technique, focusing on the strengths, shortcomings and purposes, is provided along with a review of models reported in the literature.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 13 (2009)
Issue (Month): 8 (October)
|Contact details of provider:|| Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description|
|Order Information:|| Postal: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/bibliographic|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Canyurt, Olcay Ersel & Ozturk, Harun Kemal & Hepbasli, Arif & Utlu, Zafer, 2005. "Estimating the Turkish residential-commercial energy output based on genetic algorithm (GA) approaches," Energy Policy, Elsevier, vol. 33(8), pages 1011-1019, May.
- Herriges, Joseph A. & Caves, Douglas W. & Train, K. & Windle, R. J., 1987. "A Bayesian Approach to Combining Conditional Demand and Engineering Models of Electricity Usage," Staff General Research Papers Archive 10794, Iowa State University, Department of Economics.
- Xavier Labandeira & José M. Labeaga & Miguel Rodríguez, 2005.
"A Residential Energy Demand System for Spain,"
0501, Massachusetts Institute of Technology, Center for Energy and Environmental Policy Research.
- Dennis J. Aigner & Cynts Sorooshian & Pamela Kerwin, 1984. "Conditional Demand Analysis for Estimating Residential End-Use Load Profiles," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 81-98.
- Merih Aydinalp & V. Ismet Ugursal & Alan S. Fung, 2003. "Effects of socioeconomic factors on household appliance, lighting, and space cooling electricity consumption," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 20(3), pages 302-315.
- Bartels, R. & Fiebig, D.G., 1990.
"Integrating Direct Metering And Conditional Demand Analysis Fr Estimating End-Use Loads,"
9056, Tilburg - Center for Economic Research.
- Robert Bartels & G. Fiebig, 1990. "Integrating Direct Metering and Conditional Demand Analysis for Estimating End-Use Loads," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 79-98.
- Bartels, R. & Fiebig, D., 1990. "Integrating Direct Metering and Conditional Demand Analysis for Estimating End-Use Loads," Discussion Paper 1990-56, Tilburg University, Center for Economic Research.
- Hsiao, Cheng & Mountain, Dean C & Illman, Kathleen Ho, 1995.
"A Bayesian Integration of End-Use Metering and Conditional-Demand Analysis,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 13(3), pages 315-326, July.
- Hsiao, C. & Mountain, D.C. & Ho, C.F., 1994. "A Bayesian Integration of End-Use Metering and Conditional Demand Analysis," Papers 9411, Southern California - Department of Economics.
- Michael Parti & Cynthia Parti, 1980. "The Total and Appliance-Specific Conditional Demand for Electricity in the Household Sector," Bell Journal of Economics, The RAND Corporation, vol. 11(1), pages 309-321, Spring.
- Saidur, R. & Masjuki, H.H. & Jamaluddin, M.Y., 2007. "An application of energy and exergy analysis in residential sector of Malaysia," Energy Policy, Elsevier, vol. 35(2), pages 1050-1063, February.
- Marcos Lins & Angela da Silva & Luiz Rosa, 2002. "Regional Variations in Energy Consumption of Appliances: Conditional Demand Analysis Applied to Brazilian Households," Annals of Operations Research, Springer, vol. 117(1), pages 235-246, November.
- Douthitt, Robin A., 1989. "An economic analysis of the demand for residential space heating fuel in Canada," Energy, Elsevier, vol. 14(4), pages 187-197.
- Caves, Douglas W, et al, 1987. "A Bayesian Approach to Combining Conditional Demand and Engineering Models of Electricity Usage," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 438-448, August.
- Mark Jaccard & Alison Bailie & John Nyboer, 1996. "CO2 Emission Reduction Costs in the Residential Sector: Behavioral Parameters in a Bottom-Up Simulation Model," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 107-134.
- Siller, Thomas & Kost, Michael & Imboden, Dieter, 2007. "Long-term energy savings and greenhouse gas emission reductions in the Swiss residential sector," Energy Policy, Elsevier, vol. 35(1), pages 529-539, January.
- Robert Bartels & Denzil G. Fiebig, 2000. "Residential End-Use Electricity Demand: Results from a Designed Experiment," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 51-81.
- Haas, Reinhard & Schipper, Lee, 1998. "Residential energy demand in OECD-countries and the role of irreversible efficiency improvements," Energy Economics, Elsevier, vol. 20(4), pages 421-442, September.
- Kadian, Rashmi & Dahiya, R.P. & Garg, H.P., 2007. "Energy-related emissions and mitigation opportunities from the household sector in Delhi," Energy Policy, Elsevier, vol. 35(12), pages 6195-6211, December.
- Larsen, Bodil Merethe & Nesbakken, Runa, 2004. "Household electricity end-use consumption: results from econometric and engineering models," Energy Economics, Elsevier, vol. 26(2), pages 179-200, March.
- Aydinalp-Koksal, Merih & Ugursal, V. Ismet, 2008. "Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector," Applied Energy, Elsevier, vol. 85(4), pages 271-296, April.
- Lutzenhiser, Loren, 1992. "A cultural model of household energy consumption," Energy, Elsevier, vol. 17(1), pages 47-60.
- Emery, A.F. & Kippenhan, C.J., 2006. "A long term study of residential home heating consumption and the effect of occupant behavior on homes in the Pacific Northwest constructed according to improved thermal standards," Energy, Elsevier, vol. 31(5), pages 677-693.
- Bentzen, Jan & Engsted, Tom, 2001.
"A revival of the autoregressive distributed lag model in estimating energy demand relationships,"
Elsevier, vol. 26(1), pages 45-55.
- Bentzen, J. & Engsted, T., 1999. "A Revival of the Autoregressive Distributed Lag Model in Estimating Energy Demand Relationships," Papers 99-7, Aarhus School of Business - Department of Economics.
- Fiebig, Denzil G. & Bartels, Robert & Aigner, Dennis J., 1991. "A random coefficient approach to the estimation of residential end-use load profiles," Journal of Econometrics, Elsevier, vol. 50(3), pages 297-327, December.
- Saha, G.P. & Stephenson, J., 1980. "A model of residential energy use in New Zealand," Energy, Elsevier, vol. 5(2), pages 167-175.
- Nesbakken, Runa, 1999. "Price sensitivity of residential energy consumption in Norway," Energy Economics, Elsevier, vol. 21(6), pages 493-515, December.
- Young, Denise, 2008. "When do energy-efficient appliances generate energy savings? Some evidence from Canada," Energy Policy, Elsevier, vol. 36(1), pages 34-46, January.
- Aydinalp, Merih & Ismet Ugursal, V. & Fung, Alan S., 2002. "Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks," Applied Energy, Elsevier, vol. 71(2), pages 87-110, February.
- Aydinalp, Merih & Ismet Ugursal, V. & Fung, Alan S., 2004. "Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks," Applied Energy, Elsevier, vol. 79(2), pages 159-178, October.
When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:13:y:2009:i:8:p:1819-1835. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Shamier, Wendy)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.