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In modern smarthomes, temperature regulation is achieved through a mix of traditional and emergent technologies including air conditioning, heating, intelligent utilization of the effects of sun, wind, and shade as well as using stored heat and cold. To achieve the desired comfort for the inhabitants while minimizing environmental impact and cost, the home controller must predict how its actions will impact the temperature and other environmental factors in various parts of the home. The question we are investigating in this paper is whether the temperature values in different rooms in a home are predictable based on readings from sensors in the home. We are also interested in whether increased accuracy can be achieved by adding sensors to capture the state of doors and windows of the given room and/or the whole home, and what type of machine learning algorithms can take advantage of the additional information. As experimentation on real-world homes is highly expensive, we use ScaledHome, a 1:12 scale, IoT-enabled model of a smart home for data acquisition. Our experiments show that while additional data can improve the accuracy of the prediction, the type of machine learning models needs to be carefully adapted to the number of data features available. 

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.