Segmentation and Classification of Commercial Building Occupants by Energy-Use Efficiency and Predictability

IEEE Transactions on Smart Grid

Volumn 6, Issue 3, 2015, Pages 1414-1424

  Gulbinas, Rimas ^a   Khosrowpour, Ardalan ^b   Taylor, John ^b  

^a Jacobs Technion Cornell Institute   (United States)

^b Virginia Polytechnic Institute and State University   (United States)

Author keywords

Behavior; classification; clustering; commercial building; efficiency

Indexed keywords

BUILDINGS; CLASSIFICATION (OF INFORMATION); CONSTRUCTION; EFFICIENCY; ENERGY CONSERVATION; ENERGY UTILIZATION; ENTROPY; HISTORIC PRESERVATION; OFFICE BUILDINGS;

BEHAVIOR; BEHAVIORAL INTERVENTIONS; CLUSTERING; COMMERCIAL BUILDING; CUSTOMER SEGMENTATION; ENERGY USE INTENSITIES; ENERGY-USE EFFICIENCY; QUANTITATIVE MEASURES;

ENERGY EFFICIENCY;

EID: 85027951993     PISSN: 19493053     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSG.2014.2384997     Document Type: Article

References (46)

1. (2013). Environmental Protection Agency: Buildings and Their Impact on the Environment: A Statistical Summary. [Online]. Available: http://www.epa.gov/greenbuilding/pubs/gbstats.pdf
2. (2009). Energy Information Agency: Emissions of Greenhouse Gases in the United States Report. [Online]. Available: http://www.eia.gov/environment/emissions/ghg/report/pdf/tbl3.pdf
3. (2007). United States Green Buildings Council: Buildings and Climate Change Document. [Online]. Available: http://www.usgbc.org/ShowFile.aspx?DocumentID=5033
4. (2013). Leed Rating Systems. [Online]. Available: http://www.usgbc.org/leed/certification
5. (2013). Mortgage Industry National Home Energy Rating Standards. [Online]. Available: http://www.resnet.us/professional/standards/mortgage
6. (2013). About Green Button. [Online]. Available: http://www.greenbuttondata.org/greenabout.html
7. V. Marinakis, H. Doukas, C. Karakosta, and J. Psarras, "An integrated system for buildings energy-efficient automation: Application in the tertiary sector," Appl. Energy, vol. 101, pp. 6-14, Jan. 2013.
8. C. Seligman, J. Darley, and L. Becker, "Behavioral approaches to residential energy conservation," Energy Build., vol. 1, no. 3, pp. 325-337, 1978.
9. L. Becker, "Joint effect of feedback and goal setting on performance: A field study of residential energy conservation," J. Appl. Psychol., vol. 63, no. 4, pp. 428-433, 1978.
10. M. Iyer, W. Kempton, and C. Payne, "Comparison groups on bills: Automated, personalized, energy information," Energy Build., vol. 38, no. 8, pp. 988-996, 2006.
11. P. Schultz, J. Nolan, R. Cialdini, N. Goldstein, and V. Griskevicius, "The constructive, destructive, and reconstructive power of social norms," Psychol. Sci., vol. 18, no. 5, pp. 429-434, 2007.
12. J. Nolan, P. Schultz, R. Cialdini, N. Goldstein, and V. Griskevicius, "Normative social influence is underdetected," Pers. Soc. Psychol. Bull., vol. 34, no. 7, pp. 913-923, 2008.
13. J. Froehlich, L. Findlater, and J. Landay, "The design of eco-feedback technology," in Proc. 28th ACM Int. Conf. Human Factors. Comput., Atlanta, GA, USA, Apr. 2010, pp. 1-10.
14. C. Fischer, "Feedback on household electricity consumption: A tool saving energy?" Energy Efficien., vol. 1, no. 1, pp. 79-104, 2008.
15. J. Petersen, V. Shunturov, K. Janda, G. Platt, and K. Weinberger, "Dormitory residents reduce electricity consumption when exposed to real-time visual feedback and incentives," Int. J. Sustain. High. Educ., vol. 8, no. 1, pp. 16-33, 2007.
16. G. Peschiera, J. Taylor, and J. Siegel, "Response-relapse patterns of building occupant electricity consumption following exposure to personal, contextualized and occupant peer network utilization data,"Energy Build., vol. 42, no. 8, pp. 1329-1336, 2010.
17. (2014). Home-Comed-An Exelon Company. [Online]. Available: http://www.comed.com
18. (2014). oPower. [Online]. Available: http://www. opower.com
19. H. Allcott, "Social norms and energy conservation," J. Public Econ., vol. 95, no. 9, pp. 1082-1095, 2011.
20. T. Dietz, G. Gardner, J. Gilligan, P. Stern, and M. Vandenbergh, "Household actions can provide a behavioral wedge to rapidly reduce U.S. carbon emissions," Proc. Nat. Acad. Sci., vol. 106, no. 44, pp. 18452-18456, 2009.
21. E. Azar and C. Menassa, "Agent-based modeling of occupants and their impact on energy use in commercial buildings," J. Comput. Civil Eng., vol. 26, no. 4, pp. 506-518, 2011.
22. E. Azar and C. Menassa, "A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings," Energy Build., vol. 55, pp. 841-853, Dec. 2012.
23. A. Carrico and M. Riemer, "Motivating energy conservation in the workplace: An evaluation of the use of group-level feedback and peer education," J. Environ. Psychol., vol. 31, no. 1, pp. 1-13, 2011.
24. F. Siero, A. Bakker, G. Dekker, and M. Van Den Burg, "Changing organizational energy consumption behaviour through comparative feedback," J. Environ. Psychol., vol. 16, no. 3, pp. 235-246, 1996.
25. (2012). Starbucks: Energy Conservation. [Online]. Available: http://www.starbucks.com/responsibility/environment/energy
26. (2012). United States Postal Service: Postal Service Saves Millions in Energy Costs. [Online]. Available: http://about.usps.com/news/national-releases/2012/pr12-097.htm
27. A. Costa, M. Keane, J. Torrens, and E. Corry, "Building operation and energy performance: Monitoring, analysis and optimisation toolkit,"Appl. Energy, vol. 101, pp. 310-316, Jan. 2013.
28. B. Swords, E. Coyle, and B. Norton, "An enterprise energy-information system," Appl. Energy, vol. 85, no. 1, pp. 61-69, 2008.
29. R. Gulbinas and J. Taylor, "Effects of real-time eco-feedback and organizational network dynamics on energy efficient behavior in commercial buildings," Energy Build., vol. 84, pp. 493-500, Dec. 2014.
30. W. Kleiminger, T. Staake, and S. Santini, "Occupancy detection from electricity consumption data," in Proc. 5th ACM Workshop Embedded Syst. Energy-Efficien. Build., Rome, Italy, Nov. 2013, pp. 1-8.
31. M. Milenkovic and O. Amft, "An opportunistic activity-sensing approach to save energy in office buildings," in Proc. 4th ACM Int. Conf. Future Energy Syst., Berkeley, CA, USA, May 2013, pp. 247-258.
32. Y. Agarwal et al., "Occupancy-driven energy management for smart building automation," in Proc. 2nd ACM Workshop Embedded Sens. Syst. Energy-Efficien. Build., New York, NY, USA, Nov. 2010, pp. 1-6.
33. D. Delaney, G. O'Hare, and A. Ruzzelli, "Evaluation of energyefficiency in lighting systems using sensor networks," in Proc. 1st ACM Workshop Embedded Sens. Syst. Energy-Efficien. Build., New York, NY, USA, Nov. 2009, pp. 61-66.
34. V. Erickson and A. Cerpa, "Occupancy based demand response HVAC control strategy," in Proc. 2nd ACM Workshop Embedded Sens. Syst. Energy-Efficien. Build., New York, NY, USA, Nov. 2010, pp. 7-12.
35. E. Elattar, J. Goulermas, and Q. Wu, "Electric load forecasting based on locally weighted support vector regression," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 40, no. 4, pp. 438-447, Jul. 2010.
36. A. Albert and R. Rajagopal, "Smart meter driven segmentation: What your consumption says about you," IEEE Trans. Power Syst., vol. 28, no. 4, pp. 4018-4030, Nov. 2013.
37. B. Smith, J. Wong, and R. Rajagopal, "A simple way to use interval data to segment residential customers for energy efficiency and demand response program targeting," ACEEE Sum. Study Energy Efficien. Build., vol. 5, pp. 374-386, 2012.
38. G. Chicco, R. Napoli, and F. Piglione, "Comparison among clustering techniques for electricity customer classification," IEEE Trans. Power Syst., vol. 21, no. 2, pp. 933-940, May 2006.
39. A. Mutanen, "Customer classification and load profiling based on AMR measurements," Dept. Elect. Energy Eng., Tampere Univ. Technol., Tampere, Finland, Tech. Rep. 0277, Oct. 2010.
40. R. Green, I. Staffell, and N. Vasilakos, "Divide and conquer? k-means clustering of demand data allows rapid and accurate simulations of the British electricity system," IEEE Trans. Eng. Manage., vol. 61, no. 2, pp. 251-260, May 2014.
41. G. Chicco et al., "Load pattern-based classification of electricity customers,"IEEE Trans. Power Syst., vol. 19, no. 2, pp. 1232-1239, May 2004.
42. V. Figueiredo, Z. Rodrigues, and B. Gouveia, "An electric energy consumer characterization framework based on data mining techniques,"IEEE Trans. Power Syst., vol. 20, no. 2, pp. 596-602, May 2005.
43. J. Kwac, J. Flora, and R. Rajagopal, "Household energy consumption segmentation using hourly data," IEEE Trans. Smart Grid, vol. 5, no. 1, pp. 420-430, Jan. 2014.
44. J. Hartigan and M. Wong, "Algorithm as 136: A k-means clustering algorithm,"J. Roy. Stat. Soc. C, Appl. Statist., vol. 28, no. 1, pp. 100-108, 1979.
45. (2014). Standby Power. [Online]. Available: http://standby.lbl.gov
46. M. Berges, E. Goldman, S. Mathews, L. Soibelman, and K. Anderson, "User-centered nonintrusive electricity load monitoring for residential buildings," J. Comput. Civil Eng., vol. 25, no. 4, pp. 471-480, 2011.