2017 IEEE International Conference on Consumer Electronics (ICCE) 2017
DOI: 10.1109/icce.2017.7889283
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Communicating thermostats as a tool for home energy performance assessment

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Cited by 6 publications
(2 citation statements)
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“…To alleviate this problem, we use the derived closed-form solution that does not require the discretization step (Zeifman and Roth 2016). In a more recent work, we applied this method to CT data sets from several homes and compared the estimated overall R-values with qualitative self-assessments of the home insulation levels (Zeifman, Roth, and Urban 2017). Although that comparison showed good correspondence between the estimated and HEA data, several limitations make it difficult to scale-up this initial approach:…”
Section: Model Developmentmentioning
confidence: 99%
“…To alleviate this problem, we use the derived closed-form solution that does not require the discretization step (Zeifman and Roth 2016). In a more recent work, we applied this method to CT data sets from several homes and compared the estimated overall R-values with qualitative self-assessments of the home insulation levels (Zeifman, Roth, and Urban 2017). Although that comparison showed good correspondence between the estimated and HEA data, several limitations make it difficult to scale-up this initial approach:…”
Section: Model Developmentmentioning
confidence: 99%
“…Others include features that, through learning algorithms, learn the living habits of building occupants to optimize the heating, ventilation, and air conditioning (HVAC) schedule for energy savings and occupant comfort. In essence, unlike the programmable thermostat that adjusts the temperature according to a predefined program, a smart thermostat adapts its operation depending on the user [16][17][18].…”
Section: Chapter 1 Introductionmentioning
confidence: 99%