Heating in private households is a major contributor to the emissions generated today. Heat pumps are a promising alternative for heat generation and are a key technology in achieving our goals of the German energy transformation and to become less dependent on fossil fuels. Today, the majority of heat pumps in the field are controlled by a simple heating curve, which is a naive mapping of the current outdoor temperature to a control action. A more advanced control approach is model predictive control (MPC) which was applied in multiple research works to heat pump control. However, MPC is heavily dependent on the building model, which has several disadvantages. Motivated by this and by recent breakthroughs in the field, this work applies deep reinforcement learning (DRL) to heat pump control in a simulated environment. Through a comparison to MPC, it could be shown that it is possible to apply DRL in a model-free manner to achieve MPC-like performance. This work extends other works which have already applied DRL to building heating operation by performing an in-depth analysis of the learned control strategies and by giving a detailed comparison of the two state-of-the-art control methods.
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噪声的去除或取消对成像和声学具有广泛的应用。在日常生活中,Denoising甚至可能包括对地面真理不忠的生成方面。但是,对于科学应用,denoing必须准确地重现地面真相。在这里,我们展示了如何通过深层卷积神经网络来定位数据,从而以定量精度出现弱信号。特别是,我们研究了晶体材料的X射线衍射。我们证明,弱信号是由电荷排序引起的,在嘈杂的数据中微不足道的信号,在DeNo的数据中变得可见和准确。通过对深度神经网络的监督培训,具有成对的低噪声数据,可以通过监督培训来实现这一成功。这样,神经网络就可以了解噪声的统计特性。我们证明,使用人造噪声(例如泊松和高斯)不会产生这种定量准确的结果。因此,我们的方法说明了一种实用的噪声过滤策略,可以应用于具有挑战性的获取问题。
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