T. Burns, G. Fichthorn, S. Zehtabian, S. S. Bacanli, M. Razghandi, L. Bölöni, and D. Turgut. IoT Augmented Physical Scale Model of a Suburban Home. In Proc. of the Workshop on Convergent IoT at ICC-2020, pp. 1–5, June 2020.
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Creating green homes requires us to make informed decisions in the energy management of the home. For instance, whenever possible, a comfortable temperature should be achieved through natural, energy-efficient means such as opening doors or lowering shades. To do this, the control agent needs to know the impact of the action: will opening the window raise or lower the temperature? Unfortunately, developing mathematical models of an environment as complex as a home is a significant challenge, while performing real-world experiments is costly, takes a long time and is dependent 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, together with an enclosure that models the 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 effect of specific actions affecting temperature and humidity in the home.
@inproceedings{Burns-2020-ICC-CIoT, author = "T. Burns and G. Fichthorn and S. Zehtabian and S. S. Bacanli and M. Razghandi and L. B{\"o}l{\"o}ni and D. Turgut", title = "IoT Augmented Physical Scale Model of a Suburban Home", booktitle = "Proc. of the Workshop on Convergent IoT at ICC-2020", year = "2020", month = "June", pages = "1-5", doi = "10.1109/ICCWorkshops49005.2020.9145040", abstract = { Creating green homes requires us to make informed decisions in the energy management of the home. For instance, whenever possible, a comfortable temperature should be achieved through natural, energy-efficient means such as opening doors or lowering shades. To do this, the control agent needs to know the impact of the action: will opening the window raise or lower the temperature? Unfortunately, developing mathematical models of an environment as complex as a home is a significant challenge, while performing real-world experiments is costly, takes a long time and is dependent 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, together with an enclosure that models the 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 effect of specific actions affecting temperature and humidity in the home. }, }
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