使用深度强化学习优化家用电池和太阳能光伏系统
Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning
摘要：
电池和太阳能光伏系统成本的降低导致了太阳能电池家用系统的高度采用。在这项工作中，我们使用深度确定性策略梯度算法来优化这样的系统内的电池的充电和放电行为。我们的方法输出一个连续的动作空间，当它充电和放电的电池，并能很好地在随机环境中工作。我们通过在一年内选定的几周内将单个家庭的电力支出降低到近1AUD的大型电池来展示该算法的良好性能。
英文摘要：
A lowering in the cost of batteries and solar PV systems has led to a high uptake of solar battery home systems. In this work, we use the deep deterministic policy gradient algorithm to optimise the charging and discharging behaviour of a battery within such a system. Our approach outputs a continuous action space when it charges and discharges the battery, and can function well in a stochastic environment. We show good performance of this algorithm by lowering the expenditure of a single household on electricity to almost \$1AUD for large batteries across selected weeks within a year.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
