Exploiting Implicit Neural Representation for Temperature Distribution Reconstruction in Data Centers

Temperature monitoring and prediction play a crucial role in energy consumption control within data centers (DC). However, installing numerous temperature sensors in a large DC can incur significant costs and maintenance challenges. Moreover, due to environmental constraints and measurement technology limitations, missing or distorted data may arise in the measurement process of critical parameters. To address this issue, we utilize implicit neural representation to reconstruct the temperature distribution, thereby completing the missing and distorted temperature data. In this paper, we parameterize the complex temperature distribution as a continuous function, which implicitly represents the temperature within the DC as a mapping from the spatial coordinates and active power of the cabinet in the DC to the corresponding temperature. Our proposed methods demonstrate both quantitative and qualitative effectiveness in accurately completing the temperature information and rapidly reconstructing the temperature distribution. This approach offers a novel solution for addressing missing data and data distortion issues in DC temperature measurement.