The aim of the project was to implement a machine learning model to optimise the power consumption of Ericsson’s Kista data center. The approach taken was to use a Reinforcement Learning agent trained in a simulation environment based on data specific to the data center. In this manner, the machine learning model could find interactions between parameters, both general and site specific in ways that a sophisticated algorithm designed by a human never could. In this work it was found that a neural network can effectively mimic a real data center and that the Reinforcement Learning policy "TD3" could, within the simulated environment, consistently and convincingly outperform the control policy currently in use at Ericsson’s Kista data center.
Artificial Intelligence for Data Center Power Consumption Optimisation ; Artificiell Intelligens för Energikonsumtionsoptimering i Datacenter
2021-01-01
Theses
Electronic Resource
English
Artificial Intelligence , AI , Machine Learning , ML , data center , optimisation , energy optimisation , energy , power , power optimisation , climate control , reinforcement learning , Artificiell Intelligens , datacenter , optimering , energioptimering , Computer Sciences , Datavetenskap (datalogi)
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