Abstract
There have been growing discussions on estimating and subsequently reducing the operational carbon footprint of enterprise data centers. The design and intelligent control for data centers have an important impact on data center carbon footprint. In this paper, we showcase PyDCM, a Python library that enables extremely fast prototyping of data center design and applies reinforcement learning-enabled control with the purpose of evaluating key sustainability metrics including carbon footprint, energy consumption, and observing temperature hotspots. We demonstrate these capabilities of PyDCM and compare them to existing works in EnergyPlus for modeling data centers. PyDCM can also be used as a standalone Gymnasium environment for demonstrating sustainability-focused data center control.
Abstract (translated)
近年来，对估计和后续减少企业数据中心操作碳足迹的研究逐渐增加。数据中心的设计和智能控制对数据中心的碳足迹具有重要影响。在本文中，我们展示了PyDCM，一个Python库，可用于极快原型设计数据中心，并使用强化学习实现控制，以评估包括碳排放、能源消耗和观察热点在内的关键可持续指标。我们展示了PyDCM的这些功能，并将其与能源加权控制（EnergyPlus）中的现有工作进行了比较。PyDCM还可以作为一个专注于可持续数据中心控制的独立Gymnasium环境进行展示。
URL
https://arxiv.org/abs/2404.12498
