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Most of the current game-theoretic demand-side management methods focusprimarily on the scheduling of home appliances, and the related numericalexperiments are analyzed under various scenarios to achieve the correspondingNash-equilibrium (NE) and optimal results. However, not much work is conductedfor academic or commercial buildings. The methods for optimizingacademic-buildings are distinct from the optimal methods for home appliances.In my study, we address a novel methodology to control the operation ofheating, ventilation, and air conditioning system (HVAC). With the developmentof Artificial Intelligence and computer technologies, reinforcement learning(RL) can be implemented in multiple realistic scenarios and help people tosolve thousands of real-world problems. Reinforcement Learning, which isconsidered as the art of future AI, builds the bridge between agents andenvironments through Markov Decision Chain or Neural Network and has seldombeen used in power system. The art of RL is that once the simulator for aspecific environment is built, the algorithm can keep learning from theenvironment. Therefore, RL is capable of dealing with constantly changingsimulator inputs such as power demand, the condition of power system andoutdoor temperature, etc. Compared with the existing distribution power systemplanning mechanisms and the related game theoretical methodologies, ourproposed algorithm can plan and optimize the hourly energy usage, and have theability to corporate with even shorter time window if needed.

Multi-agent Reinforcement Learning Embedded Game for the Optimization of Building Energy Control and Power System Planning