Energy Meteorology and Geoinformation Systems

Weather has a major influence on the planning and operation of energy systems. In the past, efforts primarily addressed energy consumption in the electricity and heat sector, which is primarily influenced by temperature, but also solar irradiation. At the same time, extreme weather conditions such as droughts and storm events have always threatened safety-critical effects in conventional power plants.

The transformation of the energy system towards a high proportion of weather-dependent energy sources has significantly increased its variability. In 2024, an average of more than 54% of net electricity generation in Germany was already covered by wind and solar systems, which once again clearly underlines the growing role of the weather. In addition, a wide range of cross-sector flexibility options are heavily dependent on local weather conditions in terms of generation, consumption, and energy transmission infrastructure.

Meteorology and geoinformatics, including the methods, models, and measurement campaigns based on them, are thus being used directly in the energy industry. The accuracy of the methods developed in these fields therefore has a direct impact on the security of the energy supply and the economic viability of the energy transition.

Analysis of the future energy system requires knowledge not only of existing systems, but also potential future systems. For existing systems, it is important to have historical data on localized weather and the subsequent generation and consumption to model these relationships in the future. For future plants, it is important not only to gather information on planned plant installations, but also for location-specific controllable loads like electrolysers, heat pumps, charging stations, and storage systems.

In addition to the meteorologically determined site potentials, detailed information on the local energy grid, building landscape, and socioeconomics also helps to more accurately estimate future developments. To these ends, geoinformatic methods and geographic information systems (GIS) are used and extended to link various geo-referenced data sets.

Real-time systems for forecasting energy generation and consumption ranging from the next few minutes up to days into the future have long been an established part of the day-to-day business of energy supply. The required quality of these systems has however continuously grown with the number of renewable installations. Artificial intelligence (AI) methods have proven increasingly useful in this area, as they often meet the requirements for automation and scaling.

To analyze specific sites, existing data sets are supplemented with current measurement data from remote sensing methods to better capture specific factors, e.g. local weather phenomena, shading and shadowing, or turbulence. For example, LIDAR stations provide modern wind field site measurements and satellite images provide large-scale earth observation data at good resolutions.

The capacity of overhead power lines is determined under the meteorological conditions specified in the EN 50182 standard, assuming 35 °C air temperature, 900 W/m² global radiation, and a 0.6 m/s perpendicular wind vector at the conductor cable. It’s common however for conductor cables to remain cooler, e.g. due to lower air temperatures or global radiation. We address these cooling effects are addressed by indirect methods based on weather data. In principle, the meteorological parameters of wind speed, air temperature, and global radiation are converted into a so-called continuous current carrying capacity using a conversion model like CIGRE