PLANtoACT Task 2.1: Hourly profiles - Heating and cooling demand

Regional Heating and Cooling Demand Profiles for the PLANtoACT Project

This dataset contains normalized hourly heating and cooling demand profiles developed for the PLANtoACT project (Task 2.2). The profiles represent the long-term building heating and cooling demand behaviour of five European pilot regions and are intended for use in energy system modelling, renewable energy assessment, and regional energy planning.

The dataset was generated using the Renewables.ninja weather API together with the demand_ninja building energy demand model and regional administrative boundaries. Hourly weather data (temperature, global horizontal radiation, humidity, wind speed) were downloaded for MERRA-2 grid points located within each region, converted into hourly heating and cooling demand, averaged into a representative regional profile, and normalized while preserving the long-term degree-hour equivalent — the demand-side analogue of the equivalent full-load hours used for the wind, solar, and hydro generation profiles.

The dataset accompanies the scripts available in the corresponding GitLab repository.

Study Regions

The dataset contains heating and cooling demand profiles for the following regions:

Dataset Structure

Each regional folder contains:

The dataset also includes, at the top level:

• Shapefiles/, the regional administrative boundary polygons used to select the MERRA-2 grid points for each study region;
• Normalized_Profiles_heating_2024.png and Normalized_Profiles_cooling_2024.png, which compare the normalized heating and cooling demand profiles across all study regions.

Data Generation Methodology

The regional heating and cooling demand profiles were generated according to the following workflow:

1. Regional administrative boundaries were provided as GIS shapefiles.
2. MERRA-2 grid points (0.625° × 0.5° resolution, the same spatial grid used for the wind profiles) falling within each regional polygon were identified, with farthest-point sampling applied if a region contained more points than a configurable maximum.
3. Hourly weather variables (temperature, global horizontal radiation, humidity, wind speed) were downloaded from the Renewables.ninja weather API for each selected grid point, over a five-year period (2020–2024).
4. Hourly heating and cooling demand were computed from the weather variables using the demand_ninja building energy demand model, based on a BAIT (building-adjusted internal temperature) approach with heating and cooling thresholds of 14 °C and 20 °C respectively.
5. Per-point demand series were averaged across all selected grid points to produce a representative regional profile, separately for heating and cooling.
6. Annual degree-hour integrals (the sum of hourly demand values, analogous to equivalent full-load hours for generation profiles) were calculated for each grid point and for the regional average, for every simulated year.
7. The 5-year aggregated regional profile was normalized using its own observed maximum.
8. A non-linear correction factor was applied to the most recent year to preserve the 5-year average degree-hour integral while maintaining the hourly and seasonal variability of that year.
9. Final normalized hourly profiles were exported for a leap-year (8784-hour) calendar.

Data Format

The profile files contain a single column:

Each row represents one hour of the year. The absolute demand can be reconstructed by multiplying the normalized profile by the corresponding regional degree-hour integral reported in dh_by_year_heating.csv / dh_by_year_cooling.csv.

Intended Applications

The dataset is intended for:

• Energy system modelling
• Renewable energy scenario analysis
• Regional energy planning
• Capacity expansion modelling
• Long-term electricity system simulations
• Sector coupling studies
• Academic research

Software

The dataset was generated using Python together with the following libraries:

• pandas
• NumPy
• GeoPandas
• Shapely
• SciPy
• Matplotlib
• Requests
• demand_ninja

Hourly weather data were obtained using the Renewables.ninja API. Heating and cooling demand were computed using the demand_ninja building energy demand model.

Related Software

The scripts used to generate this dataset are available from the associated GitLab repository:

PLANtoACT / task_2_1 / Heating and Cooling Demand Hourly Profiles · GitLab

Funding

This work was developed within the PLANtoACT project.

The PLANtoACT project has received funding from the European Union's LIFE Programme under Grant Agreement No. 101214506 (LIFE-2024-CET), managed by the European Climate, Infrastructure and Environment Executive Agency (CINEA).

Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union or CINEA. Neither the European Union nor CINEA can be held responsible for them.

Citation

If you use this dataset in your work, please cite both the Zenodo record and the associated software repository: PLANtoACT / task_2_1 / Heating and Cooling Demand Hourly Profiles · GitLab, https://gitlab.inf.unibz.it/plantoact/task_2_1/heating-and-cooling-demand-hourly-profiles.