Analysis of household electricity consumption behaviours: Impact of domestic electricity generation

Applied Mathematics and Computation

Volumn 270, Issue , 2015, Pages 165-178

  Motlagh, Omid ^a   Paevere, Phillip ^a   Hong, Tang Sai ^b   Grozev, George ^a  

^a CSIRO   (Australia)

^b UNIVERSITY PUTRA MALAYSIA   (Malaysia)

Author keywords

Demand side management; Domestic electricity Generation; Rebound effect; Renewables; Rooftop solar electricity; Self Consumption

Indexed keywords

DEMAND SIDE MANAGEMENT; ELECTRIC POWER UTILIZATION; ELECTRIC UTILITIES; PHOTOVOLTAIC CELLS; SELF ORGANIZING MAPS;

ELECTRICITY GENERATION; REBOUND EFFECT; RENEWABLES; SELF-CONSUMPTION; SOLAR ELECTRICITY;

ELECTRIC POWER GENERATION;

EID: 84940497922     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amc.2015.08.029     Document Type: Article

References (44)

1. ABC (2013). Rapid uptake of solar panels puts dent in electricity market, report shows, Retrieved 25 May 2014, from http://www.abc.net.au/news/2013-08-05/new-report-shows-rapid-take-up-of-solar-panels/4864954 (accessed 25.05.14).
2. Abdullah M.A., Agalgaonkar A.P., Muttaqi K.M. Probabilistic load flow incorporating correlation between time-varying electricity demand and renewable power generation Renew. Energy 55 2013 532 543 10.1016/j.renene.2013.01.010
3. Abreu J.M., Pereira F.C., Ferrao P. Using pattern recognition to identify habitual behavior in residential electricity consumption Energy Build. 49 2012 479 487
4. Al-Mumin A., Khattab O., Sridhar G. Occupants' behavior and activity patterns influencing the energy consumption in the Kuwaiti residences Energy Build. 35 6 2003 549 559
5. Ausgrid (2012). Smart grid smart city, trial dataset, retrieved on 6 July 2014, from http://www.smartgridsmartcity.com.au/About-Smart-Grid-Smart-City/Resources-and-results.aspx (accessed 06.07.14).
6. Ausgrid (2014). Smart grid smart city: Information Clearing House (ICH), https://ich.smartgridsmartcity.com.au/Main. Accessed online 22 Nov 2014.
7. Barnham K., Knorr K., Mazzer M. Benefits of photovoltaic power in supplying national electricity demand Energy Policy 54 2013 385 390 10.1016/j.enpol.2012.10.077
8. Barros C.P., Gil-Alana L.A., Payne J.E. US disaggregated renewable energy consumption: persistence and long memory behavior Energy Econ. 40 2013 425 432 10.1016/j.eneco.2013.07.018
9. Bhandari R., Stadler I. Grid parity analysis of solar photovoltaic systems in Germany using experience curves Sol. Energy 83 9 2009 1634 1644 10.1016/j.solener.2009.06.001
10. BOM (2014). Bureau of meteorology, definition of seasons, available online on http://www.bom.gov.au/climate/glossary/seasons.shtml (accessed 02.07.14).
11. Cao H.A., Beckel C., Staake T. Are domestic load profiles stable over time? An attempt to identify target households for demand side management campaigns Proceedings of the 39th Annual Conference of the IEEE Industrial Electronics Society (IECON 2013) 2013 4733 4738
12. Castillo-Cagigal M., Caamano-Martin E., Matallanas E., Masa-Bote D., Gutierrez A., Monasterio-Huelin F., Jimenez-Leube J. PV self-consumption optimization with storage and active DSM for the residential sector Sol. Energy 85 9 2011 2338 2348 10.1016/j.solener.2011.06.028
13. Chen C.S., Hsu C.T., Korimara R. The photovoltaic generation system impact on the energy demand of a small island and its financial analysis Proceedings of the 2011 2nd International Conference on Advances in Energy Engineering (ICAEE) 14 2012 411 417
14. CSIRO (2013). Change and choice: the Future Grid Forum's analysis of Australia's potential electricity pathways to 2050, Retrieved 7 May 2014, from http://www.csiro.au/Organisation-Structure/Flagships/Energy-Flagship/Future-Grid-Forum-brochure.aspx. (accessed 07.05.14)
15. Et-Tolba E., Maaroufi M., Ouassaid M. Demand side management algorithms and modeling in smart grids A customer's behavior based study Proceedings of 2013 International Renewable and Sustainable Energy Conference (IRSEC) 2013 531 536
16. FiT (2014). Feed-in tariff for new applicants: proposed feed-in tariff rates for 2015, Available online http://www.energyandresources.vic.gov.au/energy/environment-and-community/victorian-feed-in-tariff-schemes/new-feed-in-tariff (accessed 02.07.14).
17. S.F., Ghaderi, A., Azadeh, H.S., Keyno, (2007). Forecasting electricity consumption by separating the periodic variable and decompositions the pattern. IEEE International Conference on Industrial Engineering and Engineering Management, 292 - 296 2 - 4 Dec. 2007 doi: 10.1109/IEEM.2007.4419198.
18. L.A., Greening, D.L., Greene, C., Difiglio, (2000). Energy efficiency and consumption: the rebound effect - a survey, Energy Policy. 28(6-7), 389-401.
19. Hernandez L., Baladron C., Aguiar J.M., Carro B., Sanchez-Esguevillas A. Classification and clustering of electricity demand patterns in industrial parks Energies 5 12 2012 5215 5228
20. Higgins A., Grozev G., Ren Z., Garner S., Walden G., Taylor M. Modelling future uptake of distributed energy resources under alternative tariff structures Energy 74 2014 455 463
21. Hino H., Shen H.Y., Murata N., Wakao S., Hayashi Y. A versatile clustering method for electricity consumption pattern analysis in households IEEE Trans. Smart Grid 4 2 2013 1048 1057
22. Kaur A., Pedro H.T.C., Coimbra C.F.M. Impact of onsite solar generation on system load demand forecast Energy Convers. Manag. 75 2013 701 709 10.1016/j.enconman.2013.08.026
23. Keyno H.R.S., Ghaderi F., Azade A., Razmi J. Forecasting electricity consumption by clustering data in order to decrease the periodic variable's effects and by simplifying the pattern Energy Convers. Manag. 50 3 2009 829 836
24. Korenius T., Laurikkala J., Juhola M. On principal component analysis, cosine and Euclidean measures in information retrieval Inf. Sci. 177 22 2007 4893 4905
25. Martinez-Gil J., Freudenthaler B., Natschlaeger T. Modeling User Behavior through Electricity Consumption Patterns Proceedings of the 2013 24th International Workshop on Database and Expert Systems Applications (DEXA 2013) 2013 204 208
26. McLoughlin F., Duffy A., Conlon M. Characterising domestic electricity consumption patterns by dwelling and occupant socio-economic variables: an Irish case study Energy Build. 48 2012 240 248
27. Motlagh O., Foliente F., Grozev G. Knowledge-mining the Australian smart grid smart city data: a statistical-neural approach to demand-response analysis LNG&C: Planning Support Systems and Smart Cities (Chapter 10) 2015 189 207
28. Moura P.S., de Almeida A.T. Multi-objective optimization of a mixed renewable system with demand-side management Renew. Sustain. Energy Rev. 14 5 2010 1461 1468 10.1016/j.rser.2010.01.004
29. Mulder G., De Ridder F., Six D. Electricity storage for grid-connected household dwellings with PV panels Sol. Energy 84 7 2010 1284 1293 10.1016/j.solener.2010.04.005
30. Munkhammar J., Grahn P., Widen J. Quantifying self-consumption of on-site photovoltaic power generation in households with electric vehicle home charging Sol. Energy 97 2013 208 216 10.1016/j.solener.2013.08.015
31. Ndiaye D., Gabriel K. Principal component analysis of the electricity consumption in residential dwellings Energy Build. 43 2-3 2011 446 453
32. Ordenes M., Marinoski D.L., Braun P., Ruther R. The impact of building-integrated photovoltaics on the energy demand of multi-family dwellings in Brazil Energy Build. 39 6 2007 629 642 10.1016/j.enbuild.2006.10.006
33. Paevere P., Higgins A., Ren Z.G., Horn M., Grozev G., McNamara C. Spatio-temporal modelling of electric vehicle charging demand and impacts on peak household electrical load Sustain. Sci. 9 1 2014 61 76 10.1007/s11625-013-0235-3
34. Peschiera G., Taylor J.E., Siegel J.A. Response-relapse patterns of building occupant electricity consumption following exposure to personal, contextualized and occupant peer network utilization data Energy Build. 42 8 2010 1329 1336
35. Ren Z.G., Paevere P., McNamara C. A local-community-level, physically-based model of end-use energy consumption by Australian housing stock Energy Policy 49 2012 586 596 10.1016/j.enpol.2012.06.065
36. RET (2014). Renewable Energy Target, Australian, Available online, http://www.environment.gov.au/climate-change/renewable-energy-target-scheme (accessed 02.07.14).
37. Rogers D.F., Polak G.G. Optimal clustering of time periods for electricity demand-side management IEEE Trans. Power Syst. 28 4 2013 3842 3851
38. Shen H.Y., Hino, H. Murata, N. Wakao, S. Hayashi, Y. Ieee Automatic extraction of basic electricity consumption patterns in household Proceedings of the International Conference on Renewable Energy Research and Applications ICRERA 2012
39. Stedmon A.W., Winslow R., Langley A. Micro-generation schemes: user behaviours and attitudes towards energy consumption Ergonomics 56 3 2013 440 450 10.1080/00140139.2012.723140
40. Stuart G., Fleming P., Ferreira V., Harris P. Rapid analysis of time series data to identify changes in electricity consumption patterns in UK secondary schools Build. Environ. 42 4 2007 1568 1580
41. Taylor J.W., de Menezes L.M., McSharry P.E. A comparison of univariate methods for forecasting electricity demand up to a day ahead Int. J. Forecast. 22 1 2006 1 16
42. Vanhoudt D., Geysen D., Claessens B., Leemans F., Jespers L., Van Bael J. An actively controlled residential heat pump: Potential on peak shaving and maximization of self-consumption of renewable energy Renew. Energy 63 2014 531 543 10.1016/j.renene.2013.10.021
43. Wilson B. An examination of electricity consumption patterns in manufactured housing units Hous. Policy Debate 22 2 2012 175 199
44. Zhang J., Yang X.Y., Shen F., Li Y.W., Xiao H., Qi H. et al. Principal component analysis of electricity consumption factors in China Proceedings of the 2012 International Conference on Future Energy, Environment, and Materials Pt C 16 2012 1913 1918