A multilevel regression approach to understand effects of environment indicators and household features on residential energy consumption

Energy

Volumn 66, Issue , 2014, Pages 722-731

  Tso, Geoffrey K F ^a   Guan, Jingjing ^a  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Area variations; Multilevel regression; Residential energy consumption

Indexed keywords

ACOUSTIC WAVE ABSORPTION; AIR CONDITIONING; ECONOMICS; HEATING; HOUSING; REGRESSION ANALYSIS; ENERGY UTILIZATION;

AREA VARIATIONS; CONCEPTUAL FRAMEWORKS; EXPLANATORY VARIABLES; HOUSEHOLD FEATURES; MULTILEVEL REGRESSION; PHYSICAL CHARACTERISTICS; RESIDENTIAL ENERGY CONSUMPTION; STRATIFIED SAMPLING;

ENERGY UTILIZATION; HEATING;

CONCEPTUAL FRAMEWORK; FUEL CONSUMPTION; MACROECONOMICS; REGRESSION ANALYSIS; RESIDENTIAL ENERGY; ENERGY EFFICIENCY; ENVIRONMENTAL INDICATOR; HOUSEHOLD ENERGY;

UNITED STATES;

EID: 84896400304     PISSN: 03605442     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.energy.2014.01.056     Document Type: Article

References (58)

1. Energy Information Administration Residential energy consumption survey data show decreased energy consumption per household 2012, http://www.eia.gov/todayinenergy/detail.cfm?id=6570.
2. Sailor D.J. Relating residential and commercial sector electricity loads to climate - evaluating state level sensitivities and vulnerabilities. Energy 2001, 26(7):645-657.
3. Wang S., Wang S. Spatial interaction models for biomass consumption in the United States. Energy 2011, 36(11):6555-6558.
4. Dergiades T., Tsoulfidis L. Estimating residential demand for electricity in the United States, 1965-2006. Energy Econ 2008, 30(5):2722-2730.
5. Psiloglou B.E., Giannakopoulos C., Majithia S., Petrakis M. Factors affecting electricity demand in Athens, Greece and London, UK: a comparative assessment. Energy 2009, 34(11):1855-1863.
6. Dixon G., Abdel-Salam T., Kauffmann P. Evaluation of the effectiveness of an energy efficiency program for new home construction in eastern North Carolina. Energy 2010, 35(3):1491-1496.
7. Sanquist T.F., Orr H., Shui B., Bittner A.C. Lifestyle factors in US residential electricity consumption. Energy policy 2012, 42:354-364.
8. Loulou R., Goldstein G., Noble K. Documentation for the MARKAL family of models. Energy technology systems analysis programme 2004, 65-73.
9. Kainuma M., Matsuoka Y., Morita T. Climate policy assessment: Asia-Pacific integrated modeling 2003, Springer.
10. Worrell E., Ramesohl S., Boyd G. Advances in energy forecasting models based on engineering economics. Annu Rev Environ Resour 2004, 29:345-381.
11. Manne A., Mendelsohn R., Richels R. MERGE: a model for evaluating regional and global effects of GHG reduction policies. Energy policy 1995, 23(1):17-34.
12. Don H. WorldScan: the core version 1999, CPB Netherlands Bureau for Economic Policy Analysis.
13. van Ruijven B., de Vries B., van Vuuren D.P., van der Sluijs J.P. Aglobal model for residential energy use: uncertainty in calibration to regional data. Energy 2010, 35(1):269-282.
14. Amato A.D., Ruth M., Kirshen P., Horwitz J. Regional energy demand responses to climate change: methodology and application to the Commonwealth of Massachusetts. Clim Change 2005, 71(1-2):175-201.
15. Hadley S.W., Erickson D.J., Hernandez J.L., Broniak C.T., Blasing T. Responses of energy use to climate change: a climate modeling study. Geophys Res Lett 2006, 33(17).
16. Aebischer B, Catenazzi G, Henderson G, Jakob M. Impact of climate change on thermal comfort, heating and cooling energy demand in Europe. Conference impact of climate change on thermal comfort, heating and cooling energy demand in Europe. p. 859-870.
17. Henley A., Peirson J. Non-linearities in electricity demand and temperature: parametric versus non-parametric methods. Oxford Bull Econ Stat 1997, 59(1):149-162.
18. Hekkenberg M., Moll H.C., Uiterkamp A.J.M.S. Dynamic temperature dependence patterns in future energy demand models in the context of climate change. Energy 2009, 34(11):1797-1806.
19. Wilbanks T.J. Effects of climate change on energy production and use in the United States 2009, DIANE Publishing.
20. Ranjan M., Jain V.K. Modelling of electrical energy consumption in Delhi. Energy 1999, 24(4):351-361.
21. Bianco V., Manca O., Nardini S. Electricity consumption forecasting in Italy using linear regression models. Energy 2009, 34(9):1413-1421.
22. Kaza N. Understanding the spectrum of residential energy consumption: a quantile regression approach. Energy Policy 2010, 38(11):6574-6585.
23. Gelman A., Hill J. Data analysis using regression and multilevel/hierarchical models 2007, Cambridge University Press.
24. Lam C., Tang H.L., Li D.H.W. Seasonal variations in residential and commercial sector electricity consumption in Hong Kong. Energy 2008, 33(3):513-523.
25. Chow T., Leung C. Neural network based short-term load forecasting using weather compensation. IEEE Trans Power Syst 1996, 11(4):1736-1742.
26. Dash P., Liew A., Rahman S. Fuzzy neural network and fuzzy expert system for load forecasting. IEE Proc Gener Transm Distrib 1996, 143(1):106-114.
27. Islam S.M., Al-Alawi S.M., Ellithy K.A. Forecasting monthly electric load and energy for a fast growing utility using an artificial neural network. Electr Power Syst Res 1995, 34(1):1-9.
28. Khotanzad A., Davis M.H., Abaye A., Maratukulam D.J. An artificial neural network hourly temperature forecaster with applications in load forecasting. IEEE Trans Power Syst 1996, 11(2):870-876.
29. Robinson W.S. Ecological correlations and the behavior of individuals. Int J Epidemiol 2009, 38(2):337-341.
30. Swan L.G., Ugursal V.I. Modeling of end-use energy consumption in the residential sector: a review of modeling techniques. Renew Sustain Energy Rev 2009, 13(8):1819-1835.
31. ® 2011, De Gruyter.
32. Goldstein H. Multilevel statistical models 2011, Wiley. 4th ed.
33. Raudenbush S.W., Bryk A.S. Hierarchical linear models: applications and data analysis methods 2002, Sage Publications, Incorporated. 2nd ed.
34. Snijders T.A.B., Bosker R.J. Multilevel analysis: an introduction to basic and advanced multilevel modeling 2011, Sage Publications Ltd. 2nd ed.
35. Muthén B., Asparouhov T. Multilevel regression mixture analysis. JR Stat Soc Ser A Stat Soc 2009, 172(3):639-657.
36. De Leeuw J., Kreft I. Random coefficient models for multilevel analysis. JEduc Behav Stat 1986, 11(1):57-85.
37. Longford N.T. Random coefficient models 1993, Clarendon Press, Oxford.
38. Longford N.T. Afast scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested random effects. Biometrika 1987, 74(4):817-827.
39. Raudenbush S., Bryk A.S. Ahierarchical model for studying school effects. Sociol Educ 1986, 1-17.
40. Raudenbush S.W., Bryk A.S. Methodological advances in analyzing the effects of schools and classrooms on student learning. Rev Res Educ 1988, 15:423-475.
41. Littell R., Milliken G., Stroup W., Wolfinger R., Schabenberger O. SAS systems for mixed models 1996, SAS Inst. Inc, Cary, NC.
42. Hox J.J. Multilevel analysis: techniques and applications 2002, Erlbaum, Mahwah, NJ.
43. Singer J.D. Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. JEduc Behav Stat 1998, 23(4):323-355.
44. Merlo J., Chaix B., Yang M., Lynch J., Råstam L. Abrief conceptual tutorial of multilevel analysis in social epidemiology: linking the statistical concept of clustering to the idea of contextual phenomenon. JEpidemiol Commun Health 2005, 59(6):443-449.
45. Hox J.J. Multilevel analysis: techniques and applications 2010, Taylor & Francis.
46. Energy Inforamtion Administration About the RECS 2013, http://www.eia.gov/consumption/residential/about.cfm.
47. Rabe-Hesketh S., Skrondal A. Multilevel modelling of complex survey data. JR Stat Soc Ser A Statist Soc 2006, 169(4):805-827.
48. Grilli L., Pratesi M. Weighted estimation in multilevel ordinal and binary models in the presence of informative sampling designs. Survey Methodol 2004, 30:93-103.
49. Carle A.C. Fitting multilevel models in complex survey data with design weights: recommendations. BMC Med Res Methodol 2009, 9(1):49.
50. Asparouhov T. General multi-level modeling with sampling weights. Commun Stat Theory Methods 2006, 35(3):439-460.
51. Sas Institute SAS/Stat 9.2 user's guide: the Glimmix procedure (book excerpt) 2008, SAS Publishing.
52. Akaike H. Anew look at the statistical model identification. IEEE Trans Automat Contr 1974, 19(6):716-723.
53. Schwarz G. Estimating the dimension of a model. Ann Stat 1978, 6(2):461-464.
54. Hannan E.J., Quinn B.G. The determination of the order of an autoregression. JR Stat Soc Ser B Stat Methodol 1979, 190-195.
55. Bozdogan H. Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions. Psychometrika 1987, 52(3):345-370.
56. Raftery A.E. Bayesian model selection in social research. Sociol Methodol 1995, 25:111-164.
57. Ewing R., Rong F. The impact of urban form on US residential energy use. Hous Policy Debate 2008, 19(1):1-30.
58. Hox J.J., Maas C.J.M. Multilevel analysis. Encyclopedia of social measurement 2005, 785-793. Elsevier, New York. K. Kempf-Leonard (Ed.).