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Title: IoT Augmented Physical Scale Model of a Suburban Home
Green homes require informed energy management decisions. For instance, it is preferable that a comfortable internal temperature is achieved through natural, energy-efficient means such as opening doors or lowering shades as opposed to turning on the air conditioning. This requires the control agent to understand the complex system dynamics of the home: will opening the window raise or lower the temperature in this particular situation? Unfortunately, developing mathematical models of a suburban home situated in its natural environment is a significant challenge, while performing real-world experiments is costly, takes a long time and depends on external circumstances beyond the control of the experimenter. In this paper, we describe the architecture of a physical, small scale model of a suburban home and its immediate exterior environment. Specific scenarios can be enacted using Internet of Things (IoT) actuators that control the doors and windows. We use a suite of IoT sensors to collect data during the scenario. We use deep learning-based temporal regression models to make predictions about the impact of specific actions on the temperature and humidity in the home.  more » « less
Award ID(s):
1852002
NSF-PAR ID:
10225524
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
IEEE ICC 2020 Workshop on Convergent IoT (C-IoT)
Page Range / eLocation ID:
1 to 5
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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