Optimizing the thermal performance of phase change materials in building applications using deep reinforcement learning and Bayesian optimization

Abstract

This research presents a novel methodology for Deep Reinforcement Learning (DRL) and Bayesian Optimisation of the thermal performance of PCMs in building operations. The developed models utilise a unique and large dataset comprising 1500 building thermal profiles, simulated for various climates and building setups obtained from our industry partners and as open data. PCM-based systems are deployed for thermal insulation of building envelopes to regulate indoor temperature conditions and reduce the need for heating and cooling systems, resulting in enhanced energy efficiency. Through the real-time management of the thermal efficacy of PCMs using the DRL method trained on the large dataset and fine-tuning of the underlying model parameters using Bayesian Optimisation, the optimised system achieves energy saving in heating and cooling load of up to 45 percent, along with the induced reduction in CO2 emission. At the same time, DRL contributes to decreasing the thermal fluctuation in the indoor temperature and keeps it in the narrow range of 1.2 °C in case of high thermal variability scenarios. Currently, the best performance is reported in the literature. This research exemplifies the potential of DRL and Bayesian optimisation in sustainable building. It depicts the applications of advanced intelligent computing algorithms with big building energy data as a novel, robust and superior approach for optimising real-world building energy management systems. The methodology and the improvements in energy savings in thermal and energy management of buildings highlight the novelty and potential benefit of the implemented research as a new intellectual property towards sustainable building design.