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The analysis of energy scenarios for future energy systems requires appropriate data. However, while more or less detailed data on energy production is often available, appropriate data on energy consumption is often scarce. In our JERICHO-E-usage dataset, we provide comprehensive data on useful energy consumption patterns for heat, cold, mechanical energy, information and communication, and light in high spatial and temporal resolution. Furthermore, we distinguish between residential, industrial, commerce, and mobility consumers. For our dataset, we aggregate bottom-up data and disaggregate top-down data both to the NUTS2 level. The NUTS2 level serves as an interface to validate our combined method approach and the calculations. We combine a multitude of data sources such as weather time series, standard load profiles, census data, movement data, and employment figures to increase the scope, validity, and reproducibility for energy system modeling. The focus of our JERICHO-E-usage dataset on useful energy consumption might be of particular interest to researchers who analyze energy scenarios where renewable electricity is largely substituted for fossil fuel (sector coupling).

In 2011, the concept of Industry 4.0 was introduced and later adopted by the German government, paving the way for a new industrial revolution in Germany. The high significance of this topic is reflected by the large number of corresponding publications. Additionally, the regional focus of research is widespread on a global level and often differs even at a national level. This paper generates transparency regarding the adoption of the concept of Industry 4.0 by analyzing the locations of main contributors within the research field on an international, European, and German-national level. Further, it examines the regionally different foci concerning the concept of Industry 4.0. Having identified four main aspects linked to Industry 4.0 within a pre-study, a quantitative literature research was conducted based on over 800 published papers. The results were further visualized with QGIS. Looking at the results, it can be concluded that the German research community is virtually the only user of the term Industry 4.0, while other institutions seem to link their research to other related concepts. On a German level, the majority of the analyzed studies originate from Southern and Western Germany. North Rhine-Westphalia and the Aachen/Jülich region, in particular, represent main contributors.

Summary In order to cope with increasing complexity in energy systems due to rapid changes and uncertain future developments, the evaluation of multiple scenarios is essential for sound scientific system analyses. Hence, efficient modeling approaches and complexity reductions are urgently required. However, there is a lack of scientific analyses going beyond the scope of traditional energy system modeling. For this reason, we investigate the potential of metamodels to reduce the complexity of energy system modeling. In our explorative study, we investigate their potential and limits for applications in the fields of electricity dispatch and design optimization for heating systems. We first select a suitable metamodeling approach by conducting pre‐tests on a small scale. Based on this, we selected artificial neural networks due to their good performance compared to other approaches and the multiple possibilities of network topologies and hyperparameter settings. As for the dispatch model, we show that a high accuracy of price replication can be achieved while substantially reducing the runtimes per investigated scenario (from 2 hours on average down to less than 30 seconds). With the design optimization model, we find double‐edged results: while we also achieve a substantial reduction of runtime in this case (from ~0.8 hours to less than 30 seconds), the simultaneous forecasting of several interdependent variables proved to be problematic and the accuracy of the metamodel shows to be insufficient in many cases. Overall, we demonstrate that metamodeling is a suitable approach to complemement traditional energy system modeling rather than to replace them: the loss of traceability in (black‐box) metamodels indicates the importance of hybrid solutions that combine fundamental models with metamodels.

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