Generated machine data is often not fully utilized in modern power production, although it could provide new approaches to significantly increase productivity, flexibility and resource efficiency as well as energy efficiency in production. Data-based models, which can be created with the help of machine learning algorithms, can map the system’s behavior accurately and thus provide a basis for a better system understanding for further energy und resource optimization approaches. The objective of this paper is to develop a generic system identification tool that uses the above-mentioned data-based modeling approach to optimize the electrical power and resource consumption for a given load, regardless of the considered plant or machine. Therefore, the system identification tool autonomously preprocesses the data, compares different hyperparameters for neural networks to reproduce the system’s behavior and finally selects the best-suited regression algorithm with the corresponding hyperparameters.