Modelling power consumption for jobs on a machine tool is commonly performed by measuring the real power consumption of comparable jobs and machines. The so gathered data is then processed to represent the time-averaged sums of power consumptions of previous jobs. These values of power consumption are then used for upcoming comparable jobs. This approach allows for no high-resolution prediction of power consumption and further presumes static processing times of jobs. Here we propose a new approach to model power consumption that incorporates a Sequence-to-Sequence model, which generates time series according to dynamic data, that describes a numerical control code and environment settings such as state of tools, etc.