In this work, we observed the energy efficiency of AI planners. In recent times, efficient energy usage and green software development have become more important, as the energy consumption is closely tied to carbon emissions. However, the energy consumption in AI planning tools is often times overlooked, the focus is set on finding better plans faster. For the research, multiple planners were selected from international planning competitions and tested on several problems and domains. While running the planners we monitored the CPU and RAM usage and extracted the energy demand per second with Intels RAPL interface. We then compared the planner in their power demand and energy consumption by using the Green-up, Power-up and Speed-Up metric. Our analysis shows, that the faster planner is not always more energy efficient. The average power of a planner plays a significant role and should be taken more into consideration, when designing planning tools. We could also observe, that under the same hardware configurations, the memory allocation of a planner does only have a minor impact on the energy usage. In addition the analysis shows, that the key factors of the energy consumption of a planner are the CPU usage and the computation time.