The increasing complexity of the energy transition requires a high-resolution, integrated analysis of generation, demand, and infrastructure. Integrated spatio-temporal datasets enable improved matching of supply and demand through intelligent space-time management. This paper presents a systematic overview of modern methods and toolchains for generating, processing, and integrating geospatial and temporal data for energy system analysis, as well as for transformation and integration studies. The objective is to build consistent datasets from heterogeneous sources and demonstrate their value for future energy system modeling.

On the supply side, methods are presented that combine master data with satellite-based remote sensing to improve completeness and spatial resolution of decentralized renewable energy assets. On the demand side, building-level geospatial data from high-resolution 3D city models are used to derive energy states and differentiated load profiles. The integration of multiple datasets enables a holistic representation of both technical and socio-economic drivers. A further focus is the automated generation of consistent electricity and gas grid models using open geospatial data, combined with network-specific, geographic, and demographic information. This approach enables realistic grid representations, particularly in data-scarce regions.

Based on the integrated georeferenced representation of generators, grids, and loads, high-resolution time series and power forecasts of generation, demand, and power flows are computed using numerical weather prediction data as well as AI and physics-based models.

Building on the present system state, transformation scenarios are developed to forecast structural changes in generation and demand over coming decades, considering technical und sit-specific potentials, land-use constraints, yield assessments, and grid connection limitations.

Methodologically, the different tool chains integrate approaches from artificial intelligence, remote sensing, data fusion, and agent-based modeling.

For analysis, interactive GIS-based tools are applied to explore high-dimensional spatio-temporal datasets, enabling intuitive interpretation of system dynamics and supporting decision-making processes. GIS plays a key role by enabling the collection, analysis, and visualization of spatial data for both scientific and practical energy transition applications.

As application examples within this contribution, two end user tools are presented. The Transformation Atlas provides a freely accessible web-GIS platform visualizing electricity market and grid interactions with high spatial and temporal resolution. Furthermore, the Energy Transition Calculator is a planning and visualization tool based on hourly regional simulations at district level across eight weather years, including generation, demand, and flexibility use. It supports both current-state assessment and scenario analysis up to 2045 in five-year steps.