Modeling Climate Management in a Smart Home using a Scaled Testbed with Accelerated Time

IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022)(2022)

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摘要
A smart home with a controller that can understand and predict the interaction between the external environment and the user's behavior and preferences can provide significant energy efficiency and savings. Unfortunately, experimentation of real world homes for the development of such a controller is prohibitively expensive. In this paper we describe techniques through which such experiments can be performed on scaled testbed with an accelerated time. We illustrate how the modeling of different geographical areas can be performed by the mapping of the model's temperature and time to their real-world equivalents. We train three different machine learning models for predicting different sensor readings in the testbed, and find that the achieved predictive accuracy supports the feasibility of the development of future smart climate controllers.
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关键词
Internet of things, machine learning, smart home modeling, temperature prediction
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