In the face of climate change, it is a necessity to reduce the environmental impact of AI models. The pursuit of more energy-efficient AI is referred to as Green AI. A key ingredient in Green AI strategies is to reduce the amount of data used for training and inference. This is especially important for energy intensive sectors like the manufacturing industry, which is already under pressure to reduce their production-related CO2 emissions. This study evaluates empirically the impact of sample rate reduction on time series data as a possibility to implement Green AI for manufacturers that use CNC machining. A real-world dataset is used for this research to evaluate the performance of the decimation pre-processing step for our sample application, semi-supervised deep anomaly detection (DAD). The results show that decimation has the potential to assist manufacturers to reduce resource consumption and to make advances in operational sustainability. In addition, the DAD performance is not only stable, but even improves to a certain extent in the course of the sample rate conversion. This can arguably be attributed to the beneficial effect of noise reduction. This research contributes to the ongoing discourse to develop energy efficient AI methods and is especially applicable to the field of manufacturing processes.