Understanding European energy consumption with QGIS
The Energy Atlas developed by the Joint Research Centre (JRC) is a groundbreaking tool that provides high-resolution insights into the EU’s energy supply and demand. With a 1x1 km resolution, the Atlas highlights energy use in 2019 and 2021 while projecting future scenarios for 2050. But how exactly was this achieved, and how can GIS professionals replicate this workflow in QGIS?
Let’s explore the process step by step.
Source: Energy and industry geography lab (EIGL)
Step 1: Start with National Energy Data
The Energy Atlas relies on national energy balances from EUROSTAT, which provide detailed data on energy flows—from production to consumption—across sectors and energy types.
To replicate:
Obtain data: Download national energy balances or similar datasets from EUROSTAT or other governmental sources. These should include information on energy products, economic sectors, and regional statistics.
Import to QGIS: Load the data into QGIS as tables or CSV files. Visualise key variables (e.g., energy consumption by sector) by linking them to national boundaries. (Use the OSM Plugin to obtain boundary data)
QGIS Tools to Use:
Join Attributes by Field Value: To link energy data to national boundaries.
Graduated Symbology: To create maps showing energy use patterns at the country level.
Step 2: Downscale to Regional Levels
The Energy Atlas downscales national data to NUTS3 regions using auxiliary datasets, such as population density and economic activity.
To replicate:
Obtain auxiliary datasets: Download datasets like population density, GDP, or heating degree days from sources such as EUROSTAT or Copernicus. Example : Eurostat Population Density
Spatially join data: Use these datasets to proportionally distribute national energy data to regional levels.
QGIS Tools to Use:
Join Attributes by Location: To associate regional polygons with energy data.
Zonal Statistics: To summarise auxiliary data (e.g., population density) within regional boundaries.
Step 3: Map Energy Use to a 1x1 km Grid
The JRC’s workflow uses high-resolution land use and land cover data to distribute regional energy data into 1x1 km cells.
To replicate:
Create a grid: Generate a 1x1 km grid over your area of interest using Vector > Research Tools > Create Grid.
Assign energy values: Intersect the grid with regional polygons, distributing energy data proportionally using land use or industrial activity datasets (e.g., Copernicus CORINE Land Cover).
QGIS Tools to Use:
Intersect: To split regional polygons into grid cells.
Rasterise (Vector to Raster): To visualise energy data as a grid-based raster layer.
Field Calculator: To adjust energy values proportionally based on land use.
Step 4: Model Future Scenarios
The JRC used energy scenarios to project how supply and demand may change by 2050, incorporating decarbonisation policies and electrification trends.
To replicate:
Modify energy datasets: Apply forecasted trends to adjust current energy use by sector and energy type.
Distribute changes spatially: Use field calculations to update grid-level data based on projected growth or decline.
QGIS Tools to Use:
Field Calculator: To apply growth rates or reductions to energy data.
Raster Calculator: To combine different layers and simulate changes over time.
Step 5: Visualise and Analyse Results
With energy data mapped at a 1x1 km resolution, the next step is to visualise and interpret the results.
To replicate:
Classify data: Use graduated symbology to highlight areas of high, medium, and low energy consumption or demand.
Validate insights: Overlay additional datasets, such as infrastructure maps, to ensure results align with real-world observations.
Create final outputs: Design polished maps using QGIS’s Layout Manager, including essential elements like legends, scale bars, and north arrows.
QGIS Tools to Use:
Symbology: To classify energy data and apply colour ramps.
Layout Manager: To create presentation-ready maps for reports or analysis.
Conclusion
The JRC’s Energy Atlas workflow demonstrates the power of GIS in tackling complex energy challenges. By starting with national data, downscaling to regional levels, and mapping to a high-resolution grid, GIS professionals can gain valuable insights into energy supply and demand. Using QGIS, this process can be replicated to address energy transition goals or inform infrastructure planning—all while leveraging open-source tools and datasets.
Ready to take the first step? Download your datasets, fire up QGIS, and start mapping.