Energy Meteorology and Geoinformation Systems

Weather has always had a major influence on the planning and operation of the German energy system. In the past, these influences primarily included energy consumption in the electricity and heat sectors, which is strongly influenced by temperature, but also by brightness. At the same time, extreme weather conditions such as dry periods and storm events have always been able to cause safety-critical effects in the conventional power plant fleet. The transformation of the energy system towards high shares of weather-dependent energy sources has significantly increased weather dependency. In 2020, an average of more than 37 % of net electricity generation in Germany was already covered by wind and PV plants, which again clearly underscores the continuing growth of the weather's role. In addition, a wide variety of cross-sector flexibility options in generation, consumption, but also in the utilization of energy supply structures are highly dependent on local weather patterns.

Real-time systems for forecasting energy generation and consumption for the next few minutes to days into the future have already been established in the day-to-day business of energy suppliers for several years, and the demands on the quality of these systems are continuously increasing with further additions. In this topic area, artificial intelligence (AI) methods have increasingly proven useful, as they often serve the requirements in terms of automation and scaling.

In order to be able to answer energy system analytical questions on larger space-time scales, additional information about the geographically precise generation and consumption landscape is required in addition to weather conditions. On the one hand, this includes the location of the already installed plants, but also the estimation of the additions to be expected in the future. Consequently, it concerns not only the large number of additional planned renewable energy plants, but also in particular the location-specific development of controllable loads such as electrolysers, heat pumps, charging stations, and storage systems. Thus, in addition to meteorologically driven location potentials, detailed information on the energy grids, building landscape, and socio-economic effects also play an important role to more accurately estimate future developments. Methods of geoinformatics as well as geoinformation systems (GIS) are used and further developed to link different georeferenced data sets. In various thematic areas, existing data sets are also extended with current measurement data from remote sensing methods (e.g. LiDAR, drones, satellite-based earth observations) in order to better capture location specifics.

Meteorology and geoinformatics, and the methods, models and measurement campaigns based on them, are thus being directly incorporated into the energy industry. Consequently, the accuracy of the methods developed in these topics has a direct impact on the security of energy supply and the economic viability of the energy transition.


  • Meteorological influences on energy systems
  • Remote sensing methods
  • Forecasts for energy systems
  • Space-time dependencies
  • Location-specific mapping of the generation and consumption landscape
  • AI in the energy sector