Integrating reinforcement learning and predictive control for smart home energy management

To achieve carbon neutrality and reduce the dependence on fossil energy, solar panels, heat pumps and electric vehicles (EVs) are becoming commonplace in European homes. The abundant solar power, geothermal energy, as well as environmentally friendly electric vehicles, reduce the usage of fossil-based energy, while posing challenges to the home energy management system at the same time due to the intermittent feature of renewable energy and potential battery degradation from EVs. In this proposal, we propose a smart home management system that optimises the entire home energy cost while considering a set of constraints related to battery safety and reliability, driver/household demand and actuator. As an MSCA-PF fellow, Dr. Meng Yuan will receive crucial training at the Chalmers University of Technology and engage in battery power control and home energy optimisation works which span the areas of control theory and electrical engineering. Interaction between different areas will spur novel methodologies and fruitful outcomes including 1) an adaptive and robust power controller for EV battery systems, 2) a learning-based energy management algorithm that minimises the total energy cost while satisfying the household power supply, and 3) a safe learning-based control framework for home energy system management. The foreseeable results of the project will include a health-aware optimal control for vehicle battery systems that enables vehicle-to-home technology and prolongs the battery's lifetime; a new interdisciplinary energy management system combining machine learning and control algorithms to revolutionise energy sectors.