A proximal policy optimization based intelligent home solar management
Kode Creer Imitiaz Parvez
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.