Title: Multi-agent Reinforcement Learning Embedded Game for the Optimization of Building Energy Control and Power System Planning,
Abstract: Most of the current game-theoretic demand-side management methods focus
primarily on the scheduling of home appliances, and the related numerical
experiments are analyzed under various scenarios to achieve the corresponding
Nash-equilibrium (NE) and optimal results. However, not much work is conducted
for academic or commercial buildings. The methods for optimizing
academic-buildings are distinct from the optimal methods for home appliances.
In my study, we address a novel methodology to control the operation of
heating, ventilation, and air conditioning system (HVAC). With the development
of Artificial Intelligence and computer technologies, reinforcement learning
(RL) can be implemented in multiple realistic scenarios and help people to
solve thousands of real-world problems. Reinforcement Learning, which is
considered as the art of future AI, builds the bridge between agents and
environments through Markov Decision Chain or Neural Network and has seldom
been used in power system. The art of RL is that once the simulator for a
specific environment is built, the algorithm can keep learning from the
environment. Therefore, RL is capable of dealing with constantly changing
simulator inputs such as power demand, the condition of power system and
outdoor temperature, etc. Compared with the existing distribution power system
planning mechanisms and the related game theoretical methodologies, our
proposed algorithm can plan and optimize the hourly energy usage, and have the
ability to corporate with even shorter time window if needed.