The next evolution of MDE: a seamless integration of machine learning into domain modeling Advances in software and sensors have led to a new generation of systems which can help to minimize human intervention in critical infrastructures, like the power grid. However, they have mainly been designed to face predictable situations, in order to react, for example, to a critical overload. This is called known domain knowledge. However, such systems have also to face events that are unpredictable at design time. For instance, the electric consumption of a house depends on the number of persons living there, their activities, weather conditions, used devices, and so forth. Despite such behaviour is unpredictable at design time, it is identifiable and a hypothesis about it can be already formulated and solved later by observing past situations, once data becomes available. Sutcliffe et al., 1 suggest to call this known unknown.   Machine learning algorithms are designed to resolve these unknowns, using fine or coarsegrained learning. Coarsegrained learning means extracting the average behaviour of a large dataset. Conversely, finegrained learning means specializing learning algorithms only on specific elements. In cases where datasets are composed of independent and heterogenous entities, which behave very differently, finding one coarsegrained common behaviour can be difficult or even inappropriate. For example, considering smart grids, the daily consumption of a factory follows a very different pattern than the consumption of an apartment. Thus, coarsegrained learning alone, which is based on the law of large numbers, can be inaccurate for such systems. Additionally, any data changes requires the whole learning process to be recomputed. Instead, following a divide and conquer strategy, learning on finer granularities can be considerably more efficient 2, 3. In accordance to the pedagogical concept 4, we refer to small finegrained learning units as micro learning.   However, applying micro learning on systems, such as the electric grid, can potentially lead to many finegrained learning units, that need to be combined and synchronised with domain data. Learning frameworks like TensorFlow focus solely on the learning flow without any relation to the domain model. Consequently, domain data and its structure is expressed in different models than learning tasks, using different languages and tools. This leads to a separation of domain data, knowledge, known unknowns, and associated learning methods. Therefore, an appropriate structure to model learning units and their relationships to domain knowledge is required. To tame such complexity, we propose to weave micro machine learning seamlessly into data modeling. Specifically, our approach aims at: (1) Structuring complex learning tasks with reusable, chainable, and independently computable micro learning units. (2) Seamlessly integrating behavioural models which are known at design time, behavioural models that need to be learned at runtime, and domain models using common modeling concepts. (3) Automating the mapping between the mathematical representation expected by a specific machine learning algorithm and the domain representation 5 and independently updating micro learning units to be fast enough for online learning.   As a natural extension of modeldriven engineering approaches, we take advantage of relationships between domain data and behavioural elements (learned or known at design time) to implicitly define a finegrained mapping of learning units and domain data. We implemented and integrated our approach into the opensource modeling framework GreyCat, which is specifically designed for the requirements of CPSs and IoT. We evaluate our approach on a concrete smart grid case study and show that: (1) Micro machine learning for such scenarios can be more accurate than coarsegrained learning (2) Performance is fast enough to be used for realtime analytics. The full paper has been published in 6.

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 workeffort 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 implementationfeasibility an evaluation based on the stateoftheart is pursued. Furthermore, a tool prototype indicates current and future application possibilities of machine learning in the smart grid engineering process.