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Data Mining for Residential Buildings Using Smart WiFi Thermostats.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data Mining for Residential Buildings Using Smart WiFi Thermostats./
作者:	Huang, Kefan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	68 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
標題:	Energy. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28674463
ISBN:	9798534696363

Data Mining for Residential Buildings Using Smart WiFi Thermostats.
Huang, Kefan.

Data Mining for Residential Buildings Using Smart WiFi Thermostats. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 68 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Dr.Ph.)--University of Dayton, 2021.

This item must not be sold to any third party vendors.

Smart WiFi thermostats are not just a device for controlling heating and cooling comfort in buildings, they also can learn from occupant behaviors and permit occupants to control their comfort remotely. This research seeks to go beyond this state of the art by utilizing smart thermostat WiFi data from detached residences combined with outdoor condition data to develop dynamic models to predict room temperature and cooling/heating demand and then apply these models to new thermostat temperature scheduling scenarios, associated with lower energy cooling/heating. The ultimate objective of this effort is to reduce energy use in residences and demonstrate the ability to respond to peak utility demand events while maintaining thermal comfort within a minimally acceptable range. Back Propagation Neural Network (BPNN), Long-Short Term Memory (LSTM) and Encoder-Decoder LSTM approaches are used to develop these dynamic models. Results demonstrate that LSTM outperforms BPNN and Encoder-Decoder LSTM approach, yielding MAE value on testing data of less than 0.5oC, equal to the resolution error of the measured temperature and MAPE value on testing data of 0.64. Additionally, the models developed are shown to be highly accurate in predicting energy savings from aggressive thermostat setpoint schedules aimed at yielding deep reduction (up to 14.3%) in heating and cooling energy, as well as energy reduction that cooling or heating could be curtailed in response to a high demand event while maintaining thermal comfort band.

ISBN: 9798534696363Subjects--Topical Terms:

876794
Energy.
Subjects--Index Terms:

Smart thermostats

Data Mining for Residential Buildings Using Smart WiFi Thermostats.
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500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
500 $a Advisor: Hallinan, Kevin.
502 $a Thesis (Dr.Ph.)--University of Dayton, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Smart WiFi thermostats are not just a device for controlling heating and cooling comfort in buildings, they also can learn from occupant behaviors and permit occupants to control their comfort remotely. This research seeks to go beyond this state of the art by utilizing smart thermostat WiFi data from detached residences combined with outdoor condition data to develop dynamic models to predict room temperature and cooling/heating demand and then apply these models to new thermostat temperature scheduling scenarios, associated with lower energy cooling/heating. The ultimate objective of this effort is to reduce energy use in residences and demonstrate the ability to respond to peak utility demand events while maintaining thermal comfort within a minimally acceptable range. Back Propagation Neural Network (BPNN), Long-Short Term Memory (LSTM) and Encoder-Decoder LSTM approaches are used to develop these dynamic models. Results demonstrate that LSTM outperforms BPNN and Encoder-Decoder LSTM approach, yielding MAE value on testing data of less than 0.5oC, equal to the resolution error of the measured temperature and MAPE value on testing data of 0.64. Additionally, the models developed are shown to be highly accurate in predicting energy savings from aggressive thermostat setpoint schedules aimed at yielding deep reduction (up to 14.3%) in heating and cooling energy, as well as energy reduction that cooling or heating could be curtailed in response to a high demand event while maintaining thermal comfort band.
590 $a School code: 0327.
650 4 $a Energy. $3 876794
650 4 $a Behavior. $3 532476
650 4 $a Software. $2 gtt. $3 619355
650 4 $a Accuracy. $3 3559958
650 4 $a Humidity. $3 3559498
650 4 $a Regression analysis. $3 529831
650 4 $a Back propagation. $3 3681810
650 4 $a Forecasting. $3 547120
650 4 $a Heat. $3 573595
650 4 $a Time series. $3 3561811
650 4 $a Feedback. $3 677181
650 4 $a Energy consumption. $3 631630
650 4 $a Radiation. $3 673904
650 4 $a Ventilation. $3 776587
650 4 $a Building construction. $3 2071835
650 4 $a Schedules. $3 3564128
650 4 $a Neurons. $3 588699
650 4 $a Cooling. $3 1457878
650 4 $a Error correction & detection. $3 3480646
650 4 $a Power. $3 518736
650 4 $a Neural networks. $3 677449
650 4 $a Variables. $3 3548259
650 4 $a HVAC. $3 3555644
650 4 $a Algorithms. $3 536374
650 4 $a Demand side management. $3 3681811
650 4 $a Cloud computing. $3 1016782
650 4 $a Engineering. $3 586835
653 $a Smart thermostats
653 $a Wifi thermostats
653 $a Remote thermostats
653 $a Thermostat temperature scheduling
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