European Temperature
Weather-normalized daily electricity demand per European zone: per-zone regression of daily mean load on heating + cooling degree days, with the temperature component stripped out. What remains is the underlying demand trend with weather removed - what utilities and traders use to see real demand growth/decline. Joins ENTSO-E load to NASA POWER degree days.
- Source
- derived
- Licence
- Redistributable open data
- Updates
- Daily
- Data through
- 2026-07-29
- Quality
- QC clean · 0 failures
- Format
- Apache Parquet + dictionary
Safe to train on
Built only from a named, redistributable official source under a documented open licence - not scraped web data. No copyright grey zone, no personal data. Ships machine-readable Croissant metadata (ML Commons - loads in Hugging Face / Kaggle / Google), an AI training-licence manifest documenting source, licence and provenance for your model's data governance, and a machine-readable data dictionary (drop it into an agent / RAG prompt so the model knows every column) - all inspectable before you buy.
What's included
- Daily weather-normalized demand (MW), per-zone HDD+CDD regression
- Temperature effect removed → see structural demand trend
- Derived from European load (ENTSO-E) + degree days (NASA POWER)
- Source: ENTSO-E + NASA POWER (public domain / CC-BY, derived)
Schema
| Column | Type | Description | Filled | Distinct |
|---|---|---|---|---|
| zone | str | ENTSO-E zone | 100.0% | 40 |
| ts | timestamp (UTC) | Day | 100.0% | 3,132 |
| demand_norm_mw | float | Weather-normalized daily mean demand, MW | 100.0% | 70,464 |
| unit | str | Unit | 100.0% | 1 |
| res_min | int | Native resolution (daily = 1440) | 100.0% | 1 |
Sample & preview
Every purchase ships as Apache Parquet with a data dictionary and the full QC report. A free sample (first rows + schema) is downloadable here - confirm fit before you buy. Source: derived (redistributable open data; attribution passes through - see our Licence).
Don't trust screenshots - drop the sample into your notebook right now.
import pandas as pd df = pd.read_parquet("european-temperature-adjusted-demand_sample.parquet") df.info() # typed columns, gap-aware, ready to join⬇ Download free Parquet sample or CSV for Excel
Coverage: 40 zones
AT · BE · BG · CH · CZ · DE_LU · DK1 · DK2 · EE · ES · FI · FR · GR · HR · HU · IE_SEM · IT_CALA · IT_CNOR · IT_CSUD · IT_NORD · IT_SARD · IT_SICI · IT_SUD · LT · LV · NL · NO1 · NO2 · NO3 · NO4 · NO5 · PL · PT · RO · SE1 · SE2 · SE3 · SE4 · SI · SK
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