Data-Driven Optimisation Based Machine Learning for Thermal Comfort in Building Environment

Abstract

Achieving thermal comfort under minimum energy considerations has become a hot topic in the field of energy building management. The existing studies have suggested several methods to predict thermal comfort and accordingly adjust the temperature setpoints to reduce the energy. The two drawbacks of the existing studies involve the increased complexity in optimisation methods using multiple models and the lack of these methods in the optimisation approach. Our novel work proposes a data-driven optimisation solution based on machine learning (ML) to maintain thermal comfort under energy efficiency considerations. We simply infer the input from a desired output using supervised learning models as an optimisation solution. We discuss the efficiency and cost-effectiveness of the proposed solution using a public UK-schools dataset from the ASHRAE database.