Energy accounting is crucial in data centers for optimizing power provisioning, capping, and tuning. This paper introduces the Kepler framework, which estimates power consumption at the process, container, and Kubernetes pod levels. Kepler offers a set of power models applicable to various architectures and metrics. In this study, we propose a generic power model that utilizes hardware counters (HC) and realtime system power metrics (e.g., running average power limit (RAPL)) as independent variables in a regression model. Unlike previous approaches that rely on aggregate power consumption, our methodology measures individual process power consumption to train the power model. We provide step-by-step instructions to measure process power consumption in a controlled environment, considering the activation constant and load-dependent dynamic power consumption in different executions. By following the Greenhouse Gas (GHG) Protocol, our approach ensures the fair distribution of constant power among the user's processes. The results demonstrate significantly improved accuracy with a mean squared error (MSE) as low as 0.010 for the proposed method, compared with an MSE of 0.16 for a simple ratio approach and 0.92 when training the model using aggregated workload power.