A cluster analysis of energy-consuming activities in everyday life
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[1] Torsten Hägerstraand. WHAT ABOUT PEOPLE IN REGIONAL SCIENCE , 1970 .
[2] B. Lenntorp. Paths in space-time environments : a time-geographic study of movement possibilities of individuals , 1976 .
[3] A. Pred,et al. Place as Historically Contingent Process: Structuration and the Time-Geography of Becoming Places , 1984 .
[4] Jessica Henryson,et al. Energy Efficiency in Buildings Through Information - Swedish Perspective , 2000 .
[5] K. Ellegård,et al. Complexity in daily life – a 3D-visualization showing activity patterns in their contexts , 2004 .
[6] Kajsa Ellegård,et al. Capturing patterns of everyday life : presentation of the visualization method VISUAL-TimePAcTS , 2006 .
[7] Dale Southerton,et al. Analysing the Temporal Organization of Daily Life: , 2006 .
[8] Kajsa Ellegård,et al. Consumer behavior in Swedish households: routines and habits in everyday life , 2007 .
[9] Elizabeth Shove. Everyday Practice and the Production and Consumption of Time , 2009 .
[10] Mikko Kolehmainen,et al. Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data , 2010 .
[11] Joakim Widén,et al. System Studies and Simulations of Distributed Photovoltaics in Sweden , 2010 .
[12] Kajsa Ellegård,et al. VISUAL-TimePAcTS/energy use - a software application for visualizing energy use from activities performed , 2010 .
[13] Katerina Vrotsou,et al. Everyday mining : Exploring sequences in event-based data ; Utforskning av sekvenser i händelsebaserade data , 2010 .
[14] Kajsa Ellegård,et al. Linköping University Post Print Visualizing energy consumption activities as a tool for developing effective policy , 2011 .
[15] K. Ellegård,et al. Visualizing energy consumption activities as a tool for making everyday life more sustainable , 2011 .
[16] Gilbert Ritschard,et al. Analyzing and Visualizing State Sequences in R with TraMineR , 2011 .
[17] I. Rowlands,et al. A comparison of four methods to evaluate the effect of a utility residential air-conditioner load control program on peak electricity use , 2011 .
[18] Wolfgang Ketter,et al. Demand side management—A simulation of household behavior under variable prices , 2011 .
[19] Young-Il Kim,et al. Methods for generating TLPs (typical load profiles) for smart grid-based energy programs , 2011, 2011 IEEE Symposium on Computational Intelligence Applications In Smart Grid (CIASG).
[20] Gianfranco Chicco,et al. Overview and performance assessment of the clustering methods for electrical load pattern grouping , 2012 .
[21] Sarah C. Darby,et al. Social implications of residential demand response in cool temperate climates , 2012 .
[22] Matthias Studer,et al. WeightedCluster Library Manual A practical guide to creating typologies of trajectories in the social sciences with R , 2013 .
[23] Colin Fitzpatrick,et al. Demand side management of a domestic dishwasher: Wind energy gains, financial savings and peak-time load reduction , 2013 .
[24] M. Lopez-Rodriguez,et al. Analysis and modeling of active occupancy of the residential sector in Spain: An indicator of residential electricity consumption , 2013 .
[25] David Infield,et al. The evolution of electricity demand and the role for demand side participation, in buildings and transport , 2013 .
[26] Madeleine Gibescu,et al. Scenario-based modelling of future residential electricity demands and assessing their impact on distribution grids , 2013 .
[27] Yolande Strengers,et al. Smart Energy Technologies in Everyday Life , 2013 .
[28] S. N. Singh,et al. Electrical load profile analysis and peak load assessment using clustering technique , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.
[29] Sarah Royston,et al. Smart energy technologies in everyday life: smart Utopia? , 2014, Technol. Anal. Strateg. Manag..
[30] Pieter Stroeve,et al. The impact of scheduling appliances and rate structure on bill savings for net-zero energy communities: Application to West Village , 2014 .
[31] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[32] Michael E. Webber,et al. Clustering analysis of residential electricity demand profiles , 2014 .
[33] Gordon Walker,et al. The dynamics of energy demand: Change, rhythm and synchronicity , 2014 .
[34] Andrew Peacock,et al. Assessing the potential of residential demand response systems to assist in the integration of local renewable energy generation , 2014 .
[35] Tom Rodden,et al. Smart grids, smart users? The role of the user in demand side management , 2014 .
[36] Harriet Bulkeley,et al. Peak electricity demand and the flexibility of everyday life , 2014 .
[37] Joeri Naus,et al. Smart grids, information flows and emerging domestic energy practices , 2014 .
[38] J. Widén,et al. Forecasting household consumer electricity load profiles with a combined physical and behavioral approach , 2014 .
[39] Tracy Bhamra,et al. “For the times they are a-changin”: the impact of shifting energy-use practices in time and space , 2014 .
[40] K. Ellegård,et al. Dividing or sharing? A time-geographical examination of eating, labour, and energy consumption in Sweden , 2015 .
[41] Mattias Hellgren. Energy Use as a Consequence of Everyday Life , 2015 .
[42] Geert Deconinck,et al. Potential of Active Demand Reduction With Residential Wet Appliances: A Case Study for Belgium , 2015, IEEE Transactions on Smart Grid.
[43] Yolande Strengers,et al. Peak demand and the ‘family peak’ period in Australia: Understanding practice (in)flexibility in households with children , 2015 .
[44] Kajsa Ellegård,et al. Who Is Behaving? Consequences for Energy Policy of Concept Confusion , 2015 .
[45] I. Røpke,et al. SMART HOMES IN TRANSITION: Investigating the role of households in the development of smart grids in Denmark , 2015 .
[46] Jan Schoormans,et al. A real-life assessment on the effect of smart appliances for shifting households’ electricity demand , 2015 .
[47] Olena Kalyanova Larsen,et al. Household electricity demand profiles – A high-resolution load model to facilitate modelling of energy flexible buildings , 2016 .
[48] G. Gutierrez-Alcaraz,et al. Effects of demand response programs on distribution system operation , 2016 .
[49] Thomas Demeester,et al. Modeling and analysis of residential flexibility: Timing of white good usage , 2016 .
[50] Eva Heiskanen,et al. Smart grid: hope or hype? , 2015, Energy Efficiency.
[51] Stephanie Boehm. On The Formation Of Biographies In Space Time Environments , 2016 .
[52] Yolande Strengers,et al. Curious energy consumers: Humans and nonhumans in assemblages of household practice , 2016 .
[53] Jacopo Torriti,et al. Simultaneous activities in the household and residential electricity demand in Spain , 2016 .
[54] Ben Anderson,et al. Laundry, energy and time: insights from 20 years of time-use diary data in the United Kingdom , 2016 .
[55] J. G. Slootweg,et al. Responsiveness of residential electricity demand to dynamic tariffs: Experiences from a large field test in the Netherlands , 2016 .