This paper presents a simulation model to estimate the instantaneous power consumption of a mobile robot by taking into account both its mechanical and computational components. The simulation model is adaptable to be tuned based on the level of accuracy needed for estimating the power consumption for the robot and the simulation time penalty. This makes a multi-fidelity power estimation tool for the robot with the capability to run-time changing the fidelity according to environmental conditions and internal computational capabilities. Such multi-fidelity energy prediction is suitable for run-time predictive decision making in a wide range of usages such as training process in model-based Reinforcement Learning (RL) as well as decision making in Model Predictive Control (MPC). The experimental results show that the simulation accurately estimates energy consumption at different fidelity levels. Higher-fidelity models closely match real-world measurements, while lower-fidelity models trade some accuracy for faster predictions. Therefore, higher estimation precision comes at the cost of increased computation.