J.E. Morrissey, S. Axon, R. Aiesha, J. Hillman, A. Revez, B. Lennon, Identification and behaviour change initiatives, 2016.
Abrahamse, W., Steg, L., Vlek, C., & Rothengatter, T. (2005). A review of intervention studies aimed at household energy conservation. Journal of Environmental Psychology, 25(3), 273-291. doi:10.1016/j.jenvp.2005.08.002
Shove, E. (2003). Converging Conventions of Comfort, Cleanliness and Convenience. Journal of Consumer Policy, 26(4), 395-418. doi:10.1023/a:1026362829781
[+]
J.E. Morrissey, S. Axon, R. Aiesha, J. Hillman, A. Revez, B. Lennon, Identification and behaviour change initiatives, 2016.
Abrahamse, W., Steg, L., Vlek, C., & Rothengatter, T. (2005). A review of intervention studies aimed at household energy conservation. Journal of Environmental Psychology, 25(3), 273-291. doi:10.1016/j.jenvp.2005.08.002
Shove, E. (2003). Converging Conventions of Comfort, Cleanliness and Convenience. Journal of Consumer Policy, 26(4), 395-418. doi:10.1023/a:1026362829781
Steemers, K., & Yun, G. Y. (2009). Household energy consumption: a study of the role of occupants. Building Research & Information, 37(5-6), 625-637. doi:10.1080/09613210903186661
Cosar-Jorda, P., Buswell, R. A., & Mitchell, V. A. (2018). Determining of the role of ventilation in residential energy demand reduction using a heat-balance approach. Building and Environment, 144, 508-518. doi:10.1016/j.buildenv.2018.08.053
Shipworth, M., Firth, S. K., Gentry, M. I., Wright, A. J., Shipworth, D. T., & Lomas, K. J. (2010). Central heating thermostat settings and timing: building demographics. Building Research & Information, 38(1), 50-69. doi:10.1080/09613210903263007
Buswell, R., Webb, L., Mitchell, V., & Leder Mackley, K. (2016). Multidisciplinary research: should effort be the measure of success? Building Research & Information, 45(5), 539-555. doi:10.1080/09613218.2016.1194601
Pink, S., Mackley, K. L., & Moroşanu, R. (2013). Hanging out at home: Laundry as a thread and texture of everyday life. International Journal of Cultural Studies, 18(2), 209-224. doi:10.1177/1367877913508461
Yan, D., O’Brien, W., Hong, T., Feng, X., Burak Gunay, H., Tahmasebi, F., & Mahdavi, A. (2015). Occupant behavior modeling for building performance simulation: Current state and future challenges. Energy and Buildings, 107, 264-278. doi:10.1016/j.enbuild.2015.08.032
Hargreaves, T. (2011). Practice-ing behaviour change: Applying social practice theory to pro-environmental behaviour change. Journal of Consumer Culture, 11(1), 79-99. doi:10.1177/1469540510390500
Aminikhanghahi, S., & Cook, D. J. (2016). A survey of methods for time series change point detection. Knowledge and Information Systems, 51(2), 339-367. doi:10.1007/s10115-016-0987-z
Strengers, Y. (2011). Negotiating everyday life: The role of energy and water consumption feedback. Journal of Consumer Culture, 11(3), 319-338. doi:10.1177/1469540511417994
HM Government, Industrial Strategy: Building a Britain fit for the future, (2017). https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/664563/industrial-strategy-white-paper-web-ready-version.pdf?_ga=2.176869477.131331705.1566905390-1346621436.1566905390.
Darby, S. J. (2017). Smart electric storage heating and potential for residential demand response. Energy Efficiency, 11(1), 67-77. doi:10.1007/s12053-017-9550-3
Department for Business Energy & Industrial Strategy, Energy consumption in the UK, 2016.
Perez, K. X., Cetin, K., Baldea, M., & Edgar, T. F. (2017). Development and analysis of residential change-point models from smart meter data. Energy and Buildings, 139, 351-359. doi:10.1016/j.enbuild.2016.12.084
Frederiks, E. R., Stenner, K., Hobman, E. V., & Fischle, M. (2016). Evaluating energy behavior change programs using randomized controlled trials: Best practice guidelines for policymakers. Energy Research & Social Science, 22, 147-164. doi:10.1016/j.erss.2016.08.020
Gynther, L., Mikkonen, I., & Smits, A. (2011). Evaluation of European energy behavioural change programmes. Energy Efficiency, 5(1), 67-82. doi:10.1007/s12053-011-9115-9
Behavioural Insights Team, Behaviour Change and Energy Use, Cabinet Off. London(Available at)/Http//Www.Cabinetoffice.Gov.Uk/Resourcelibrary/Behaviour-Change-and-Energy-UseS. (2011).
Pink, S., & Mackley, K. L. (2012). Video and a Sense of the Invisible: Approaching Domestic Energy Consumption through the Sensory Home. Sociological Research Online, 17(1), 87-105. doi:10.5153/sro.2583
Belaïd, F., Bakaloglou, S., & Roubaud, D. (2018). Direct rebound effect of residential gas demand: Empirical evidence from France. Energy Policy, 115, 23-31. doi:10.1016/j.enpol.2017.12.040
Wilson, C., Hargreaves, T., & Hauxwell-Baldwin, R. (2017). Benefits and risks of smart home technologies. Energy Policy, 103, 72-83. doi:10.1016/j.enpol.2016.12.047
Mogles, N., Walker, I., Ramallo-González, A. P., Lee, J., Natarajan, S., Padget, J., … Coley, D. (2017). How smart do smart meters need to be? Building and Environment, 125, 439-450. doi:10.1016/j.buildenv.2017.09.008
Hargreaves, T., Nye, M., & Burgess, J. (2010). Making energy visible: A qualitative field study of how householders interact with feedback from smart energy monitors. Energy Policy, 38(10), 6111-6119. doi:10.1016/j.enpol.2010.05.068
Darby, S. J. (2017). Smart technology in the home: time for more clarity. Building Research & Information, 46(1), 140-147. doi:10.1080/09613218.2017.1301707
Lynham, J., Nitta, K., Saijo, T., & Tarui, N. (2016). Why does real-time information reduce energy consumption? Energy Economics, 54, 173-181. doi:10.1016/j.eneco.2015.11.007
Owens, S., & Driffill, L. (2008). How to change attitudes and behaviours in the context of energy. Energy Policy, 36(12), 4412-4418. doi:10.1016/j.enpol.2008.09.031
Seem, J. E. (2007). Using intelligent data analysis to detect abnormal energy consumption in buildings. Energy and Buildings, 39(1), 52-58. doi:10.1016/j.enbuild.2006.03.033
Richardson, I., Thomson, M., Infield, D., & Clifford, C. (2010). Domestic electricity use: A high-resolution energy demand model. Energy and Buildings, 42(10), 1878-1887. doi:10.1016/j.enbuild.2010.05.023
Meyers, R. J., Williams, E. D., & Matthews, H. S. (2010). Scoping the potential of monitoring and control technologies to reduce energy use in homes. Energy and Buildings, 42(5), 563-569. doi:10.1016/j.enbuild.2009.10.026
Weiss, M., Patel, M. K., Junginger, M., & Blok, K. (2010). Analyzing price and efficiency dynamics of large appliances with the experience curve approach. Energy Policy, 38(2), 770-783. doi:10.1016/j.enpol.2009.10.022
Yu, Z., Haghighat, F., Fung, B. C. M., & Yoshino, H. (2010). A decision tree method for building energy demand modeling. Energy and Buildings, 42(10), 1637-1646. doi:10.1016/j.enbuild.2010.04.006
Richardson, I., Thomson, M., & Infield, D. (2008). A high-resolution domestic building occupancy model for energy demand simulations. Energy and Buildings, 40(8), 1560-1566. doi:10.1016/j.enbuild.2008.02.006
Haldi, F., & Robinson, D. (2008). On the behaviour and adaptation of office occupants. Building and Environment, 43(12), 2163-2177. doi:10.1016/j.buildenv.2008.01.003
Bacher, P., & Madsen, H. (2011). Identifying suitable models for the heat dynamics of buildings. Energy and Buildings, 43(7), 1511-1522. doi:10.1016/j.enbuild.2011.02.005
Santamouris, M., Mihalakakou, G., Patargias, P., Gaitani, N., Sfakianaki, K., Papaglastra, M., … Zerefos, S. (2007). Using intelligent clustering techniques to classify the energy performance of school buildings. Energy and Buildings, 39(1), 45-51. doi:10.1016/j.enbuild.2006.04.018
Van Raaij, W. F., & Verhallen, T. M. M. (1983). Patterns of residential energy behavior. Journal of Economic Psychology, 4(1-2), 85-106. doi:10.1016/0167-4870(83)90047-8
Papakostas, K. T., & Sotiropoulos, B. A. (1997). Occupational and energy behaviour patterns in Greek residences. Energy and Buildings, 26(2), 207-213. doi:10.1016/s0378-7788(97)00002-9
Aminikhanghahi, S., & Cook, D. J. (2019). Enhancing activity recognition using CPD-based activity segmentation. Pervasive and Mobile Computing, 53, 75-89. doi:10.1016/j.pmcj.2019.01.004
Krishnan, N. C., & Cook, D. J. (2014). Activity recognition on streaming sensor data. Pervasive and Mobile Computing, 10, 138-154. doi:10.1016/j.pmcj.2012.07.003
Wei, S., Jones, R., & de Wilde, P. (2014). Driving factors for occupant-controlled space heating in residential buildings. Energy and Buildings, 70, 36-44. doi:10.1016/j.enbuild.2013.11.001
R. Buswell, L. Webb, P. Cosar-Jorda, D. Marini, S. Brownlee, M. Thomson, S.-.H. Yang, R. Kalawsky, LEEDR project home energy dataset, (2018). doi:10.17028/rd.lboro.6176450.v1.
Kulinskaya, E., & Koricheva, J. (2010). Use of quality control charts for detection of outliers and temporal trends in cumulative meta-analysis. Research Synthesis Methods, 1(3-4), 297-307. doi:10.1002/jrsm.29
Hyndman, R. J., & Khandakar, Y. (2008). Automatic Time Series Forecasting: TheforecastPackage forR. Journal of Statistical Software, 27(3). doi:10.18637/jss.v027.i03
D. Montgomery, Introduction to statistical quality control, 2009. doi:10.1002/1521-3773(20010316)40:6<9823::AID−ANIE9823>3.3.CO;2-C.
Barbeito, I., Zaragoza, S., Tarrío-Saavedra, J., & Naya, S. (2017). Assessing thermal comfort and energy efficiency in buildings by statistical quality control for autocorrelated data. Applied Energy, 190, 1-17. doi:10.1016/j.apenergy.2016.12.100
Haines, V., & Mitchell, V. (2014). A persona-based approach to domestic energy retrofit. Building Research & Information, 42(4), 462-476. doi:10.1080/09613218.2014.893161
Eon S.E., Annual report2017, n.d.https://www.eon.com/content/dam/eon/eon-com/investors/annual-report/EON_GB17_EN.pdf(accessed December 21, 2018).
Paradiso, F., Paganelli, F., Giuli, D., & Capobianco, S. (2016). Context-Based Energy Disaggregation in Smart Homes. Future Internet, 8(1), 4. doi:10.3390/fi8010004
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