Efficacy of temporal and spatial abstraction for training accurate machine learning models: A case study in smart thermostats

Smart thermostats are increasingly popular in homes and buildings as they improve occupant comfort, lower energy use in heating and cooling systems, and reduce utility bills by automatically adjusting room temperature according to measurements of their built-in sensors. To maximize energy savings, many of these thermostats use machine learning (ML) for more accurate prediction and optimal control. These ML models must be trained for each individual building to ensure higher thermal comfort and energy savings, owing to the fact that buildings are custom-built, can be located in different climates, and may have unique occupancy patterns. This kind of training requires a significant amount of energy and sharing raw data emitted by the sensors in each building. To address these issues, we propose a novel methodology to train accurate and personalized thermal models for each home with a minimal energy footprint. Specifically, we use temporal and spatial abstraction to downsample sensor data and cluster homes with similar characteristics to train representative thermal models for each cluster. These models are customized for each home using meta-learning, achieving personalized models with high accuracy. Additionally, combining multi-step ahead prediction with our proposed abstraction technique would permit resource-constrained devices (e.g., microcontrollers) to accurately forecast indoor temperature for larger time intervals with negligible computation overhead. Experiments with smart thermostat data from 1,000 homes show that our methodology offers accurate and personalized thermal models with substantial savings in network bandwidth and training energy. To be specific, our methodology is approximately 384 (300) times more energy efficient than training an LSTM (RNN) model using the conventional approaches. Given the anticipated increase in the demand for smart thermostats and the fact that thermal models must be (re)trained regularly (e.g., every season), the proposed methodology could significantly reduce the environmental impact of training ML models for thermal comfort optimization in the long run.