TRR 391: Spatio-temporal Statistics for the Transition of Energy and Transport

Basic data for this project

Description

The Collaborative Research Center/Transregio TRR 391 models, estimates and predicts spatio-temporal processes occurring in economic and technical applications. It exploits the formal similarity of relevant statistical problems for methodological synergies and develops key techniques for the analysis of spatio-temporal data, which will enable efficient data-based decision-making in various areas of the energy and transport transition in the next decades. The reduction of CO2 emissions and the transition to renewable energies are important global challenges. Many aspects of our future life will be affected by decisions about the organization of the energy and transport transition. To be widely accepted in society, the positive effects of these measures must outweigh potential negative impacts on employment, mobility, the supply of goods, energy costs, and, generally, on prosperity. To surmount these challenges, decision-making must be based on solid empirical evidence so that its impact on whole economies, multi-national bodies, and people’s everyday life can be accurately predicted. Due to the growing digitalization, such decisions can be made on the basis of an increasing amount of massive data, often collected automatically at many different locations in space and time. Obtaining relevant and reliable insights from such extensive spatio-temporal data presents significant challenges for statistics. This does not only demand a thorough modeling of the various types of temporal and spatial dependencies, but also the development of novel beyond-state-of-the-art statistical and machine learning approaches. In TRR 391, we address these challenges and develop novel and innovative statistical methodologies for spatio-temporal data analysis to support data-based decision-making in important technological and economic settings. Considering a wide range of highly relevant prototypical applications from the areas of energy and transport, a joint perspective is pursued to lift the specific application-driven statistical problems to a general methodological level, to identify similarities, and to use these synergies for the development of fundamental statistical theory for spatio-temporal data analysis. By means of an interdisciplinary approach, combining expertise from various fields, we can thus provide data analytic solutions for concrete problems that go far beyond the state of the art and catalyze new methodological developments. Inter alia, our results will support decision-making by new simulation tools for modeling transport logistics, by precise forecasting of wind and solar power generation, and by a more reliable control of electrical energy grids. They will yield a better understanding of individual energy use and mobility behavior, of the impact of environmental policies on energy prices, and will improve the management of logistics and supply chain networks.