Abstract
Climate change has raised consciousness of the need to use cleaner energy instead of fossil fuels. Meanwhile, a change of consciousness regarding resource use has to be achieved, which can be triggered by energy consumption monitoring studies that also provide useful recommendations for energy saving. Over time, researchers have identified behaviors by monitoring energy consumption in households, but these studies are usually limited to the number of monitored households and/or to the geographical region in which the monitoring takes place. In this research work, a study with a global reach is proposed to mitigate these limitations. Using a hierarchical clustering algorithm, three distinct groups were identified using the collected data, representative of distinct behaviors. The results illustrate several behaviors regarding energy consumption, like cold temperatures, seasonal behaviors, wake up hour, stay-at-home periods, and standby device consumption.
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Acknowledgments
This work is partially supported by iCIS project (CENTRO-07-ST24-FEDER-002003) which is co-financed by QREN, in the scope of the Mais Centro Program and FEDER.
References
Abreu, J., and Pereira, F. C. (2012). “Discussion about household electricity consumption routines and tailored feedback.” ACEEE Summer Study on Energy Efficiency in Buildings, ACEEE, Washington, DC, 1–9.
Abreu, J. M., Pereira, F. C., and Ferro, P. (2012). “Using pattern recognition to identify habitual behavior in residential electricity consumption.” Energy Build., 49, 479–487.
Abreu, P. H., Silva, D. C., Almeida, F., and Mendes-Moreira, J. (2014a). “Improving a simulated soccer teams performance through a memory-based collaborative filtering approach.” Appl. Soft Comput., 23, 180–193.
Abreu, P. H., Silva, D. C., Portela, J., Mendes-Moreira, J., and Reis, L. P. (2014b). “Using model-based collaborative filtering techniques to recommend the expected best strategy to defeat a simulated soccer opponent.” Intell. Data Anal., 18(5), 973–991.
Australian Government Department of Resources, and Tourism. (2013). “Energy in Australia.” 〈http://www.bree.gov.au/documents/publications/energy-in-aust/bree-energyinaustralia-2013.pdf〉.
Belbin, L., and McDonald, C. (1993). “Comparing three classification strategies for use in ecology.” J. Veg. Sci., 4(3), 341–348.
Eurostat. (2013a). “Electricity price by country.” 〈http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Electricity_and_natural_gas_price_statistics〉.
Eurostat. (2013b). “Minimum wages by country.” 〈http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Minimum_wage_statistics/ptConchado〉.
Facebook. (2015). “Facebook news room.” 〈http://newsroom.fb.com/company-info/〉.
Foster, D., Lawson, S., Blythe, M., and Cairns, P. (2010). “Wattsup?: motivating reductions in domestic energy consumption using social networks.” Proc., 6th Nordic Conf. on Human-Computer Interaction: Extending Boundaries, 178–187.
Gaspar, R., and Antunes, D. (2011). “Energy efficiency and appliance purchases in Europe: Consumer profiles and choice determinants.” Energy Policy, 39(11), 7335–7346.
Groot, E., Spiekman, M., and Opstelten, I. (2008). “Dutch research into user behaviour in relation to energy use of residences.” Proc., 25th Int. Conf. on Passive and Low Energy Architecture, Office of Public Works, Dublin, Ireland, 5.
Heimbach, I., Gottschlich, J., and Hinz, O. (2015). “The value of user’s facebook profile data for product recommendation generation.” Electron Markets, 25(2), 125–138.
Hens, H., Parijs, W., and Deurinck, M. (2010). “Energy consumption for heating and rebound effects.” Energy Build., 42(1), 105–110.
İşyapar, M. T. (2013). “Classification of electricity customers based on real consumption values using data mining and machine learning techniques and its corresponding applications.” M.S. thesis, Middle East Technical Univ., Turkey.
Joerges, B. (1979). “Consumer energy research: An international bibliography.” International Institute for Environment and Society, Berlin.
Kaufman, L., and Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis, Wiley, Hoboken, NJ.
Kent, M., and Leaver, T., eds. (2014). An education in Facebook?: Higher education and the world’s largest social network, Routledge, New York.
Kirschner, P. A., and Karpinski, A. C. (2010). “Facebook© and academic performance.” Comput. Hum. Behav., 26(6), 1237–1245.
Knijnenburg, B. P., Willemsen, M. C., and Broeders, R. (2014). “Smart sustainability through system satisfaction: Tailored preference elicitation for energy-saving recommenders.” 20th Americas Conf. on Information Systems, SAP, Walldorf, Germany, 19.
Leth-Petersena, S., and Togebyb, M. (2001). “Demand for space heating in apartment blocks: Measuring effects of policy measures aiming at reducing energy consumption.” Energy Econ., 23(4), 387–403.
MATLAB [Computer software]. MathWorks, Natick, MA.
McDougall, G. H. G., Claxton, J. D., Ritchie, J. R. B., and Anderson, C. D. (1981). “Consumer energy research: A review.” J. Consumer Res., 8(3), 343–354.
Olofsson, T., Andersson, S., and Sjšgren, J. (2009). “Building energy parameter investigations based on multivariate analysis.” Energy Build., 41(1), 71–80.
Paauw, J., Roossien, B., Aries, M., and Santin, O. G. (2009). “Energy pattern generator—Understanding the effect of user behaviour on energy systems.” Panels of the Energy Efficiency and Behaviour Conf., European Council for an Energy Efficient Economy, Stockholm, Sweden, 11.
Santin, O. (2011). “Behavioural patterns and user profiles related to energy consumption for heating.” Energy Build., 43(10), 2662–2672.
Santin, O., Itard, L., and Visscher, H. (2009). “The effect of occupancy and building characteristics on energy consumption for space and water heating in dutch residential stock.” Energy Build., 41(11), 1223–1232.
Sokal, R., and Michener, C. (1958). “A statistical method for evaluating systematic relationships.” Univ. Kansas Sci. Bull., 38, 1409–1438.
Sokal, R. R., and Rohlf, F. J. (1962). “A statistical method for evaluating systematic relationships.” Taxon, 11(2), 33–40.
Uchiyama, I. (2006). “Hierarchical clustering algorithm for comprehensive orthologous-domain classification in multiple genomes.” Nucleic Acids Res., 34(2), 647–658.
van Raaj, F., and Verhalle, T. (1983). “A behavioral model of residential energy use.” J. Econ. Psychol., 3(1), 39–63.
Wilcoxon, F. (1945). “Individual comparisons by ranking methods.” Biom. Bull., 1(6), 80–83.
Wilson, D. R., and Martinez, T. R. (1997). “Improved heterogeneous distance functions.” J. Artif. Intell. Res., 6, 1–34.
Yu, Z., Fung, B., Haghighat, F., Yoshino, H., and Morofsky, E. (2011). “A systematic procedure to study the influence of occupant behavior on building energy consumption.” Energy Build., 43(6), 1409–1417.
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Published In
Journal of Energy Engineering
Volume 142 • Issue 4 • December 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Apr 25, 2015
Accepted: Nov 6, 2015
Published online: Jan 27, 2016
Discussion open until: Jun 27, 2016
Published in print: Dec 1, 2016
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