Demystifying Thermal Comfort in Smart Buildings: An Interpretable Machine Learning Approach

Abstract

Thermal comfort is a key consideration in smart buildings and a number of comfort models are available nowadays to evaluate the comfort level of occupants. However, the models are often complex and hardly interpretable for the developers and operators. Indeed, the model interpretations are beneficial in multifold such as for system inspection and optimization. In this article, we propose an interpretable thermal comfort system to introduce interpretability to any black-box comfort models. First, we focus on the relationship between a model's input features and output comfort level. The feature impact on comfort is investigated and the impact patterns are shown to be diverse for different features. Second, we unveil the model mechanisms about the data processing inside the model by building the model surrogates based on the interpretable machine learning algorithms. The surrogates offer outstanding fidelity for simulating the actual model mechanisms and the interpretations based on the surrogates are intuitive and informative. Our interpretable comfort system can be integrated with the existing building management systems. Accordingly, we can ease building owner's concerns about adopting new black-box technologies and enable various smart building applications like smart energy management.