论文标题
通过深度加强学习，在中国进行最佳地区供暖温度控制
Towards Optimal District Heating Temperature Control in China with Deep Reinforcement Learning
论文作者
论文摘要
在中国地区供暖网络中实现效率的提高，从而减少了碳足迹，需要超越当前行业工具的新最佳控制方法。为了关注二级网络，我们提出了一种数据驱动的深入增强学习（DRL）方法来解决此任务。我们建立了一个经过模拟数据训练的经常性神经网络，以预测室内温度。然后，该模型用于训练两种具有或没有专家指导的DRL代理，以最佳控制供应水温。与优化的基线策略相比，我们在多区域设置中的测试表明，这两种代理都可以确保更高的热舒适度，同时又具有较小的能源成本。
Achieving efficiency gains in Chinese district heating networks, thereby reducing their carbon footprint, requires new optimal control methods going beyond current industry tools. Focusing on the secondary network, we propose a data-driven deep reinforcement learning (DRL) approach to address this task. We build a recurrent neural network, trained on simulated data, to predict the indoor temperatures. This model is then used to train two DRL agents, with or without expert guidance, for the optimal control of the supply water temperature. Our tests in a multi-apartment setting show that both agents can ensure a higher thermal comfort and at the same time a smaller energy cost, compared to an optimized baseline strategy.
