Applying Machine Learning Concepts to Enhance the Smart Grid Engineering Process

The expansion of renewable energy sources, as an effort to reduce global warming and to guarantee a sustainable energy supply, forces the electrical energy systems into enhanced complexity through new requirements, actors, technological approaches or business models. This complexity is also noticed in the smart grid engineering process, resulting in increasing effort and costs. By applying machine learning concepts on the engineering process it is possible to decrease the work-effort and minimize tedious and error prone manual tasks. This work introduces three machine learning concepts and shows how they can improve the smart grid engineering process by applying a clustering approach to give recommendations of standards that are useful for the developed use case. According to their implementation-feasibility an evaluation based on the state-of-the-art is pursued. Furthermore, a tool prototype indicates current and future application possibilities of machine learning in the smart grid engineering process.