European Weather

What a heatwave or cold snap cost (or saved) each European power-market zone yesterday, priced at the day-ahead clearing price: the weather-driven load deviation from the temperature-normalized demand baseline (MW), that deviation monetised (EUR/day), and a trailing 90-day z-score of the monetised anomaly. A price-weighted anomaly, not the grid's realised balancing/imbalance cost - built strictly look-ahead-free (the z-score at day t uses only days before t).

What's included

• 3 variables per zone x day: weather_driven_load_mw, weather_driven_cost_eur, cost_anomaly_z
• weather_driven_load_mw = actual load - temperature-normalized demand baseline (MW)
• weather_driven_cost_eur = weather_driven_load_mw x day-ahead price x 24h (EUR/day, can be negative when weather suppressed demand)
• cost_anomaly_z = trailing 90-day z-score (min_periods=30), shift(1)'d - strictly no look-ahead
• Zones = the 3-way intersection of european-electricity-load, european-temperature-adjusted-demand and european-day-ahead-prices
• Derived from ENTSO-E (CC-BY) - attribute ENTSO-E

Schema

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).

import pandas as pd
df = pd.read_parquet("european-weather-power-cost_sample.parquet")
df.info()   # typed columns, gap-aware, ready to join