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Energy and AI 2024年第3期17卷 327-341页

作者： Kristina Dabrock Noah Pflugradt jann michael weinand Detlef Stolten Forschungszentrum Jülich GmbH Institute of Climate and Energy SystemsJülich Systems Analysis52425 JülichGermany RWTH Aachen University Chair for Fuel CellsFaculty of Mechanical Engineering52062 AachenGermany

Building geometry data is crucial for detailed, spatially-explicit analyses of the building stock in energy systems analysis and beyond. Despite the existence of diverse datasets and methods, a standardized and validated approach for creating a nation-wide unified and complete dataset of German building heights is not yet available. This study develops and validates such a methodology, combining different data sources for building footprints and heights and filling gaps in height data using an XGBoost machine learning algorithm. The XGBoost model achieves a mean absolute error of 1.78 m at the national level and between 1.52 m and 3.47 m at the federal state level. The goal is proving the applicability of the methodology at a large scale and creating a useful dataset. The resulting dataset is thoroughly evaluated on a building-by-building level and spatially resolved statistics on the quality of the dataset are reported. This detailed validation found that the building number and footprint area of German building stock is 90.31 % and 94.84 % correct, respectively, and the building height accuracy is 0.59 m at the national level. However, errors are not homogeneous across Germany and further research is needed into the impact of including additional datasets, especially for regions and building types with lower accuracies. This study proves that the chosen methodology is useful for generating a building height dataset and the workflow, with some modifications for regional data availability, can be transferred to other countries. The generated building dataset for Germany constitutes a valuable data basis for the research community in fields such as energy research, urban planning and building decarbonization policy development.

关键词： Machine learning XGBoost Building height Building footprint 3-D building data Geodata Spatial analysis

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Opportunities for large language models and discourse in engineering

Energy and AI

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Energy and AI 2024年第3期17卷 136-142页

作者： Jan Göpfert jann M.weinand Patrick Kuckertz Detlef Stolten Institute of Energy and Climate Research–Techno-economic Systems Analysis(IEK-3) Forschungszentrum Jülich GmbHJülich52425Germany Chair for Fuel Cells RWTH Aachen UniversityFaculty of Mechanical EngineeringAachen52062Germany

In recent years, large language models have achieved breakthroughs on a wide range of benchmarks in natural language processing and continue to increase in performance. Recently, the advances of large language models have raised interest outside the natural language processing community and could have a large impact on daily life. In this paper, we pose the question: How will large language models and other foundation models shape the future product development process? We provide the reader with an overview of the subject by summarizing both recent advances in natural language processing and the use of information technology in the engineering design process. We argue that discourse should be regarded as the core of engineering design processes, and therefore should be represented in a digital artifact. On this basis, we describe how foundation models such as large language models could contribute to the design discourse by automating parts thereof that involve creativity and reasoning, and were previously reserved for humans. We describe how simulations, experiments, topology optimizations, and other process steps can be integrated into a machine-actionable, discourse-centric design process. As an example, we present a design discourse on the optimization of wind turbine blades. Finally, we outline the future research that will be necessary for the implementation of the conceptualized framework.

关键词： Product development process Conceptual design Design methodology Design generation Natural language processing Foundation models Multi-modal models

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A systematic review of spatial disaggregation methods for climate action planning

Energy and AI

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Energy and AI 2024年第3期17卷 458-469页

作者： Shruthi Patil Noah Pflugradt jann M.weinand Detlef Stolten Jürgen Kropp Institute of Energy and Climate Research Techno-economic Systems Analysis(IEK-3)Forschungszentrum JülichWilhelm-Johnen-StraßeJülich52425NRWGermany University of Potsdam Institute for Environmental Science and GeographyKarl-Liebknecht-Str.24-25Potsdam-Golm14476BrandenburgGermany Chair for Fuel Cells RWTH Aachen Universityc/o IEK-3Forschungszentrum JülichJülich52425NRWGermany Potsdam Institute for Climate Impact Research(PIK) Member of the Leibniz AssociationP.O.Box 601203PotsdamD-14412BrandenburgGermany

National-level climate action plans are often formulated broadly. Spatially disaggregating these plans to individual municipalities can offer substantial benefits, such as enabling regional climate action strategies and for assessing the feasibility of national objectives. Numerous spatial disaggregation approaches can be found in the literature. This study reviews and categorizes these. The review is followed by a discussion of the relevant methods for the disaggregation of climate action plans. It is seen that methods employing proxy data, machine learning models, and geostatistical ones are the most relevant methods for the spatial disaggregation of national energy and climate plans. The analysis offers guidance for selecting appropriate methods based on factors such as data availability at the municipal level and the presence of spatial autocorrelation in the *** the urgency of addressing climate change escalates, understanding the spatial aspects of national energy and climate strategies becomes increasingly important. This review will serve as a valuable guide for researchers and practitioners applying spatial disaggregation in this crucial field.

关键词： Spatial downscaling Proxy data Mass-preserving Climate action plans Spatial autocorrelation Machine learning Geostatistical models

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