Building a sustainable Exascale machine is a very promising target in High Performance Computing (HPC). To tackle the energy consumption challenge while continuing to provide tremendous performance, the HPC community have rapidly adopted GPU-based systems. Today, GPUs have became the most prevailing components in the massively parallel HPC landscape thanks to their high computational power and energy efficiency. Modeling the energy consumption of applications running on GPUs has gained a lot of attention for the last years. Alas, the HPC community lacks simple yet accurate simulators to predict the energy consumption of general purpose GPU applications. In this work, we address the prediction of the energy consumption of CUDA kernels via simulation. We propose in this paper a simple and lightweight energy model that we implemented using the open-source framework SimGrid. Our proposed model is validated across a diverse set of CUDA kernels and on two different NVIDIA GPUs (Tesla M2075 and Kepler K20Xm). As our modeling approach is not based on performance counters or detailed-architecture parameters, we believe that our model can be easily approved by users who take care of the energy consumption of their GPGPU applications.