As the demand for AI-driven workloads increases, the energy consumption of Graphics Processing Units (GPUs) devices has come under intense scrutiny, particularly in hyperscale data centers where large numbers of accelerators are centralized and leased to diverse clients.

In an effort to raise awareness on the increasing carbon emissions of Cloud computing, the European Corporate Sustainability Reporting Directive effectively requires providers to supply their customers with an assessment of the carbon impact associated with their use. This represents a challenge for bare metal servers, where the deployment of dedicated power meters is often unfeasible at scale. To address this, we present PPEM-BM, a novel sensor-driven modeling approach to estimate the power consumption of bare metal servers using CPU temperature data acquired via IPMI. PPEM-BM enhances and generalizes the existing PowerHeat method, which correlates CPU temperature with power. Our methodology involves training individual power models, performing cross-evaluation to determine their portability, and then using a Learning to Rank (LTR) model to select the most appropriate pre-trained model for a target server based on its hardware configuration and CPU temperature statistics. An experiment conducted on$\mathbf{1, 0 7 6}$production servers at OVHcloud shows that PPEM-BM demonstrates a significant improvement compared to models based solely on hardware profiles. The approach offers a practical, scalable, and cost-effective solution for hosting providers to monitor energy consumption without widespread sensor deployment.