A Configurable Pythonic Data Center Model for Sustainable Cooling and ML Integration

Autor:	Naug, Avisek, Guillen, Antonio, Gutierrez, Ricardo Luna, Gundecha, Vineet, Ghorbanpour, Sahand, Mousavi, Sajad, Babu, Ashwin Ramesh, Sarkar, Soumyendu
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	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. Comment: NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning https://www.climatechange.ai/papers/neurips2023/15. arXiv admin note: substantial text overlap with arXiv:2310.03906
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.12498 Zobrazit plný text záznamu View this record from Arxiv