Regression analysis for prediction of residential energy consumption

Renewable and Sustainable Energy Reviews

Volumn 47, Issue , 2015, Pages 332-343

  Fumo, Nelson ^a   Rafe Biswas, M A ^a  

^a UNIVERSITY OF TEXAS AT TYLER   (United States)

Author keywords

Energy consumption; Energy regression models; Linear regression; Residential buildings

Indexed keywords

ENGINEERING RESEARCH; HOUSING; LINEAR REGRESSION; MEAN SQUARE ERROR;

ENERGY; ENERGY CONSUMPTION MODEL; ENERGY MODEL; ENERGY REGRESSION MODEL; ENERGY-CONSUMPTION; REGRESSION MODELLING; RESIDENTIAL BUILDING; RESIDENTIAL ENERGY CONSUMPTION; RESIDENTIAL SECTORS; SUPPORT ENERGY;

ENERGY UTILIZATION;

EID: 84925855723     PISSN: 13640321     EISSN: 18790690     Source Type: Journal    
DOI: 10.1016/j.rser.2015.03.035     Document Type: Review

References (60)

1. International Energy Outlook. The U.S. Energy Information Administration. From: ; 2013 (retrieved 01.06.14).
2. Lukas G. Swan, and V. Ismet Ugursal Modeling of end-use energy consumption in the residential sector: a review of modeling techniques Renew Sustain Energy Rev 13 2009 1819 1835
3. Building America Program, Office of Efficiency and Renewable Energy, the U.S. Department of Energy. Available at: .
4. Gang Li, Yunho Hwang, and Reinhard Radermacher Review of cold storage materials for air conditioning application Int J Refrigeration 35 8 2012 2053 2077
5. Gang Li, Yunho Hwang, and Reinhard Radermacher Experimental investigation on energy and exergy performance of adsorption cold storage for space cooling application Int J Refrigeration 44 2014 23 35
6. Nelson Fumo A review on the basics of building energy estimation Renew Sustain Energy Rev 31 2014 53 60
7. Arnaud Grandjean, Jerome Adnot, and Guillaume Binet A review and an analysis of the residential electric load curve models Renew Sustain Energy Rev 16 2012 6539 6565
8. Arash Kialashaki, and John R. Reisel Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks Appl Energy 108 2013 271 280
9. Eduardo Krüger, and Baruch Givoni Predicting thermal performance in occupied dwellings Energy Build 36 2004 301 307
10. Jiayin Zhu, and Bin Chen Simplified analysis methods for thermal responsive performance of passive solar house in cold area of China Energy Build 67 2013 445 452
11. Olofsson Thomas, Sjögren Jan-Ulric, Andersson Staffan.Energy performance of buildings evaluated with multivariate analysis. In: Proceedings of the ninth international IBPSA conference. Montréal, Canada; August 15-18, 2005.
12. Tiberiu Catalina, Vlad Iordache, and Bogdan Caracaleanu Multiple regression model for fast prediction of the heating energy demand Energy Build 57 2013 302 312
13. Alvin C. Rencher, and William F. Christensen Methods of multivariate analysis 2012 John Wiley and Sons New Jersey
14. Samprit Chatterjee, and Ali S. Hadi Regression analysis by example 5th ed 2012 Wiley Hoboken, New Jersey
15. Sanford Weisberg Applied linear regression 4th ed 2013 Wiley Hoboken, New Jersey
16. Christian Ritz, and Jens Carl Streibig Nonlinear regression with R 2008 Springer New York
17. Alan Julian Izenman Modern multivariate statistical techniques: regression, classification, and manifold learning 2008 Springer New York
18. SPSS software, IBM. Available at: (last accessed November 2014).
19. SAS®, SAS Institute Inc. Available at: (last accessed November 2014).
20. SIMCA, Umetrics Inc. Available at: (last accessed November 2014).
21. STATISTICA, StatSoft Inc. Available at: (last accessed November 2014).
22. R, Institute for Statistics and Mathematics, Wirtschaftsuniversität Wien. Available at: (last accessed November 2014).
23. STATGRAPHICS®, StatPoint Technologies, Inc. Available at: (last accessed November 2014).
24. NCSS Statistical Analysis and Graphics software, NCSS, LLC. Available at: (last accessed November 2014).
25. Carsten F. Dormann, Jane Elith, Sven Bacher, Carsten Buchmann, Gudrun Carl, and Gabriel Carré et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance Ecography 36 1 2013 27 46
26. Geoffrey K.F. Tso, and Jingjing Guan A multilevel regression approach to understand effects of environment indicators and household features on residential energy consumption Energy 66 2014 722 731
27. Home Energy Yardstick, Energy Star program. The U.S. Environmental Protection Agency. (last accessed May 2014).
28. Residential Energy Consumption Survey (RECS) End-Use Models FAQs. The U.S. Energy Information Administration. From: ; 2013 (retrieved 01.06.14).
29. Stig Hammarsten A critical appraisal of energy-signature models Appl Energy 26 1987 97 110
30. Ari Rabl, and Anne Rialhe Energy signature models for commercial buildings: test with measured data and interpretation Energy Build 19 1992 143 154
31. Lorenzo Belussi, and Ludovico Danza Method for the prediction of malfunctions of buildings through real energy consumption analysis: holistic and multidisciplinary approach of energy signature Energy Build 55 2012 715 720
32. Frances W. Yu, and Kam T. Chan Energy signatures for assessing the energy performance of chillers Energy Build 37 2005 739 746
33. Karl-Erik Westergren, Hans Högberg, and Urban Norlén Monitoring energy consumption in single-family houses Energy Build 29 1999 247 257
34. Weather Warehouse. Available at: (last accessed May 2014).
35. Weather data depot. Available at: (last accessed May 2014).
36. Robert Bartels, and Denzil G. Fiebig Metering and modeling residential end-use electricity load curves J. Forecast 15 1996 415 426
37. Dennis J. Aigner, Cyrus Sorooshian, and Pamela Kerwin Conditional demand analysis for estimating residential end-use profiles Energy J 5 1984 81 97
38. Michael Parti, and Cynthia Parti The total and appliance specific conditional demand for electricity in the household sector Bell J Econ 11 1980 309 324
39. Gaétan Lafrance, and Dominique Perron Evolution of residential electricity demand by end-use in Quebec 1979-1989: a conditional demand analysis Energy Stud Rev 6 2 1994 164 173
40. Merih Aydinalp-Koksal, and V. Ismet Ugursal Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector Appl Energy 85 2008 271 296
41. Guy R. Newshamn, and Cara L. Donnelly A model of residential energy end-use in Canada: using conditional demand analysis to suggest policy options for community energy planners Energy Policy 59 2013 133 142
42. Raffio Gregory, Isambert Ovelio, Mertz George, Schreier Charlie, Kissock Kelly. Targeting residential energy assistance. In: Proceedings of the energy sustainability, July 27-30, 2007. Long Beach, California, USA.
43. Tiberiu Catalina, Joseph Virgone, and Eric Blanco Development and validation of regression models to predict monthly heating demand for residential buildings Energy Build 40 2008 1825 1832
44. Božidar Soldo, Primož Potočnik, Goran Šimunović, Tomislav Šarić, and Edvard Govekar Improving the residential natural gas consumption forecasting models by using solar radiation Energy Build 69 2014 498 506
45. Nehad Elsawaf, Tarek Abdel-Salam, and Leslie Pagliari Evaluation of heat pumps usage and energy savings in residential buildings Int J Energy Environ 3 2012 399 408
46. Jihoon Min, Zeke Hausfather, and Qi Feng Lin A high-resolution statistical model of residential energy end use characteristics for the United States J Ind Ecol 14 2010 791 807
47. Jun Chen, Xiaohong Wang, and Koen Steemers A statistical analysis of a residential energy consumption survey studyin Hangzhou, China Energy Build 66 2013 193 202
48. Joachim Schleich, Marian Klobasa, Sebastian Gölz, and Marc Brunner Effects of feedback on residential electricity demand - findings from a field trial in Austria Energy Policy 61 2013 1097 1106
49. Will Gans, Anna Alberini, and Alberto Longo Smart meter devices and the effect of feedback on residential electricity consumption: evidence from a natural experiment in Northern Ireland Energy Econ 36 2013 729 743
50. Alessio Mastrucci, Olivier Baume, Francesca Stazi, and Ulrich Leopold Estimating energy savings for the residential building stock of an entire city: a GIS-based statistical downscaling approach applied to Rotterdam Energy Build 75 2014 358 367
51. Hongguang Nie, and René Kemp Index decomposition analysis of residential energy consumption in China: 2002-2010 Appl Energy 121 2014 10 19
52. Vincenzo Bianco, Oronzio Manca, and Sergio Nardini Linear regression models to forecast electricity consumption in Italy Energy Sources Part B 8 2013 86 93
53. Demba Ndiaye, and Kamiel Gabriel Principal component analysis of the electricity consumption in residential dwellings Energy Build 43 2011 446 453
54. Celina Filippína, Florencia Ricard, and S. Flores Larsen Evaluation of heating energy consumption patterns in the residential building sector using stepwise selection and multivariate analysis Energy Build 66 2013 571 581
55. Green Button Program. Available at: (last accessed November 2014).
56. BEopt™ (Building Energy Optimization). National Renewable Energy Laboratory. Available at: (last accessed November 2014).
57. Autotune, Oak Ridge National Laboratory. Available at: (last accessed November 2014).
58. TxAIRE Research and Demonstration Houses. The University of Texas at Tyler. Available at: (last accessed May 2014).
59. Building Energy Optimization software (BEopt™). National Renewable Energy Laboratory. Available at: (last accessed January 2015).
60. EnergyPlus, Energy Efficiency and Renewable Energy Office of the U.A. Department of Energy. Available at: (last accessed January 2015).