A proximal policy optimization based intelligent home solar management
This addresses a domain-specific problem for prosumers in smart grids, but it appears incremental as it applies an existing reinforcement learning method (PPO) to this context.
The paper tackles the problem of maximizing profits for prosumers in a smart grid by selling unused electricity under dynamic market conditions, achieving over 30% improvement in total profits compared to naive algorithms.
In the smart grid, the prosumers can sell unused electricity back to the power grid, assuming the prosumers own renewable energy sources and storage units. The maximizing of their profits under a dynamic electricity market is a problem that requires intelligent planning. To address this, we propose a framework based on Proximal Policy Optimization (PPO) using recurrent rewards. By using the information about the rewards modeled effectively with PPO to maximize our objective, we were able to get over 30\% improvement over the other naive algorithms in accumulating total profits. This shows promise in getting reinforcement learning algorithms to perform tasks required to plan their actions in complex domains like financial markets. We also introduce a novel method for embedding longs based on soliton waves that outperformed normal embedding in our use case with random floating point data augmentation.