A proximal policy optimization based intelligent home solar management
CoRR(2024)
摘要
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.
更多查看译文
关键词
Proximal Policy Optimization,Smart Home Solar Energy,Soliton Embeddings
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要