This dataset provides a benchmark for short term energy forecasting models, combining electrical load measurements from Dutch DSO Liander with predictors like corresponding weather data from OpenMeteo, day-ahead electricity prices from ENTSO-E, and profiles of electricity consumption from Energiedatawijzer. The dataset covers the full year 2024 (2024-01-01 to 2025-01-01 UTC) and includes 55 different points in the grid across the Netherlands at various levels within the grid.

The dataset is designed for developing and validating short-term energy forecasting models, particularly those that incorporate weather variables. It serves as a standardized benchmark for comparing different short term forecasting approaches in the energy domain.

Dataset Details

Curated by: OpenSTEF
License: Creative Commons BY 4.0 (CC BY 4.0). See below for specific source data licenses.
Data Period: 2024-01-01 to 2025-01-01
Temporal Resolution: 15-minute intervals for load measurements and profiles, hourly for weather data and prices (interpolated to 15-minute intervals)
Geographic Coverage: 55 points in the grid across the Netherlands in Liander service area
Total Size: ~3-6M data points across all components

Dataset Components

The dataset consists of six main components:

1. Load Measurements (`load_measurements/`)

Electrical load (active power) measurements from various types of infrastructure managed by Dutch DSO Liander. All measurements are recorded at 15-minute intervals.

Location Types:

mv_feeder (Medium Voltage Feeders): Outgoing medium voltage cables from primary substations
station_installation (Station Installations): Various primary substation installations
transformer (Transformers): Power transformers at primary substations
solar_park (Solar Parks): Anonymized and normalized individual solar park measurements
wind_park (Wind Parks): Anonymized and normalized individual wind park measurements

Solar and wind park data includes a 2-day availability delay to simulate data availability constraints at Dutch DSOs.

Column	Type	Unit	Description
timestamp	datetime64[ns, UTC]	-	Measurement timestamp in UTC
load	float64	W	Electrical load in watts
available_at	datetime64[ns, UTC]	-	Data availability timestamp

2. Weather Measurements (`weather_measurements/`)

Historical weather measurements from OpenMeteo for each load measurement location, providing ground truth weather conditions.

Column	Type	Unit	Description
temperature_2m	float32	°C	Air temperature at 2 meters above ground
relative_humidity_2m	float32	%	Relative humidity at 2 meters above ground
surface_pressure	float32	hPa	Atmospheric pressure at surface level
cloud_cover	float32	%	Total cloud cover as area fraction
wind_speed_10m	float32	km/h	Wind speed at 10 meters above ground
wind_direction_10m	float32	°	Wind direction at 10 meters above ground
shortwave_radiation	float32	W/m²	Shortwave solar radiation
direct_radiation	float32	W/m²	Direct solar radiation on horizontal plane
diffuse_radiation	float32	W/m²	Diffuse solar radiation
direct_normal_irradiance	float32	W/m²	Direct solar radiation on normal plane

3. Weather Forecasts (`weather_forecasts/`)

Latest available weather forecasts from OpenMeteo (short horizon). These represent the best available forecast at each time point.

This component is useful for simple forecasting experiments but is not fully realistic for benchmarking since it does not simulate real-world forecast availability.

Column	Type	Unit	Description
temperature_2m	float32	°C	Air temperature at 2 meters above ground
relative_humidity_2m	float32	%	Relative humidity at 2 meters above ground
surface_pressure	float32	hPa	Atmospheric pressure at surface level
cloud_cover	float32	%	Total cloud cover as area fraction
wind_speed_10m	float32	km/h	Wind speed at 10 meters above ground
wind_speed_80m	float32	km/h	Wind speed at 80 meters above ground
wind_direction_10m	float32	°	Wind direction at 10 meters above ground
shortwave_radiation	float32	W/m²	Shortwave solar radiation
direct_radiation	float32	W/m²	Direct solar radiation on horizontal plane
diffuse_radiation	float32	W/m²	Diffuse solar radiation
direct_normal_irradiance	float32	W/m²	Direct solar radiation on normal plane

4. Versioned Weather Forecasts (`weather_forecasts_versioned/`)

Time-versioned weather forecasts with lead times up to 7 days ahead, simulating real-world data availability. This component provides the most realistic forecasting scenario.

This enables realistic evaluation where forecasts are only available at specific times with specific lead times, matching real-world operational constraints.

Column	Type	Unit	Description
timestamp	datetime64[ns, UTC]	-	Target forecast timestamp
available_at	datetime64[ns, UTC]	-	When the forecast was available/created
temperature_2m	float32	°C	Air temperature at 2 meters above ground
relative_humidity_2m	float32	%	Relative humidity at 2 meters above ground
surface_pressure	float32	hPa	Atmospheric pressure at surface level
cloud_cover	float32	%	Total cloud cover as area fraction
wind_speed_10m	float32	km/h	Wind speed at 10 meters above ground
wind_speed_80m	float32	km/h	Wind speed at 80 meters above ground
wind_direction_10m	float32	°	Wind direction at 10 meters above ground
shortwave_radiation	float32	W/m²	Shortwave solar radiation
direct_radiation	float32	W/m²	Direct solar radiation on horizontal plane
diffuse_radiation	float32	W/m²	Diffuse solar radiation
direct_normal_irradiance	float32	W/m²	Direct solar radiation on normal plane

5. EPEX Day-Ahead Prices (`EPEX.parquet`)

Day-ahead electricity prices for the Netherlands from ENTSO-E Transparency Platform, providing market price signals that influence energy consumption patterns.

Column	Type	Unit	Description
timestamp	datetime64[ns, UTC]	-	Price delivery timestamp in UTC
available_at	datetime64[ns, UTC]	-	When the price was published/available
price	float64	€/MWh	Day-ahead electricity price in euros per megawatt hour

6. Electricity Consumption Profiles (`profiles.parquet`)

Standardized electricity consumption profiles from Energiedatawijzer for various customer categories in the Netherlands, representing typical usage patterns throughout the year. These values are typically normalized to sum to 1 over the year. There are 15 types of profiles, which can be read as follows: {category}_{type}_{direction}, where category says something about the connection type, type indicates whether it is a connection with or without infeed, and direction indicates whether it is a consumption or generation profile (we only include consumption profiles as infeed says something about previous year's generation). For a full description of the profiles, see the Energiedatawijzer documentation.

Column	Type	Unit	Description
timestamp	datetime64[ns, UTC]	-	Profile timestamp in UTC
available_at	datetime64[ns, UTC]	-	Data availability timestamp
{profiles}	float64	-	15 profiles for different categories

Uses

This dataset is intended for energy forecasting research, providing a standardized benchmark for comparing different forecasting approaches in the energy domain. The dataset supports various forecasting horizons and scenarios:

Operational Forecasting: 15-minute to 24-hour ahead load predictions
Day-ahead Congestion Management: Using weather forecasts for next-day congestion predictions
Multi-modal Forecasting: Combining multiple infrastructure types and weather variables
Uncertainty Quantification: Using versioned forecasts to assess prediction uncertainty
Weather-Energy Relationship Studies: Analyzing correlations between weather variables and electrical load

This dataset is compatible with various forecasting frameworks, including OpenSTEF (Open Short Term Energy Forecasting), classical time series models, machine learning approaches, and deep learning models.

Dataset Structure

The dataset is organized in the following directory structure:

liander2024/
├── liander2024_targets.yaml           # Location metadata with coordinates
├── load_measurements/                 # Electrical load data
│   ├── mv_feeder/                     # Medium voltage feeder measurements
│   ├── station_installation/          # Substation installation measurements  
│   ├── transformer/                   # Transformer measurements
│   ├── solar_park/                    # Anonymized solar park measurements
│   └── wind_park/                     # Anonymized wind park measurements
├── weather_measurements/              # Historical weather data
│   └── [same subdirectory structure as above]
├── weather_forecasts/                 # Latest weather forecasts
│   └── [same subdirectory structure as above]
├── weather_forecasts_versioned/       # Time-versioned weather forecasts
│   └── [same subdirectory structure as above]
├── EPEX.parquet                       # Day-ahead electricity prices
└── profiles.parquet                   # Electricity consumption profiles

Each subdirectory contains individual Parquet files for each location, named according to the location identifier.

Target Metadata (`liander2024_targets.yaml`)

The liander2024_targets.yaml file contains metadata for all 55 forecasting targets in the dataset. Each target includes:

Field	Type	Description
name	string	Unique identifier for the location/asset
group_name	string	Infrastructure type: `mv_feeder`, `transformer`, `station_installation`, `solar_park`, or `wind_park`
latitude	float	Approximate latitude coordinate*
longitude	float	Approximate longitude coordinate*
description	string	Human-readable description of the location
benchmark_start	datetime	Start of the benchmark evaluation period
benchmark_end	datetime	End of the benchmark evaluation period
train_start	datetime	Start of the training data period
upper_limit	float	98th percentile of load values (W)
lower_limit	float	2nd percentile of load values (W)

* Location coordinates are approximate and only based on the name of the target.

Dataset Creation

Source Data

Liander Historical Measurements

Source: Liander Open Data - Historical 15-minute Operational Measurements
License: See custom disclaimer
Description: 15-minute electrical load measurements from various infrastructure types across Liander's service territory
Modifications made: Converted into standardized Parquet format, removed _normalized suffix from load column, added available_at timestamps.

OpenMeteo Weather Data

Source: OpenMeteo Historical Weather API
License: CC BY 4.0
Description: Historical weather measurements and forecasts using the best available weather models

ENTSO-E Day-Ahead Prices

Source: ENTSO-E Transparency Platform
License: CC BY 4.0
Description: Day-ahead electricity prices for the Netherlands (EPEX Spot NL)
Modifications made: Converted into Parquet format, converted to UTC, added available_at timestamp based on availability of day ahead prices in NL.

Energiedatawijzer Consumption Profiles

Source: Energiedatawijzer - Profielen elektriciteit 2024
License: None, but permission granted for use in this dataset
Description: Standardized electricity consumption profiles for various customer categories in the Netherlands
Modifications made: Converted into Parquet format, converted to UTC added available_at timestamp, removed infeed profiles, used first hour of the year to fill the last hour of the year to get a full UTC year.

Location coordinates are approximate and may not represent exact facility locations. Solar and wind park data is normalized and anonymized for privacy. Weather data is interpolated from hourly to 15-minute resolution to match load measurements.

How to Use

You can load the dataset files directly into pandas dataframes:

import pandas as pd

load_data = pd.read_parquet("hf://datasets/OpenSTEF/liander2024-stef-benchmark/load_measurements/mv_feeder/OS Edam.parquet")
weather_data = pd.read_parquet("hf://datasets/OpenSTEF/liander2024-stef-benchmark/weather_measurements_versioned/mv_feeder/OS Edam.parquet")
epex = pd.read_parquet("hf://datasets/OpenSTEF/liander2024-stef-benchmark/EPEX.parquet")
profiles = pd.read_parquet("hf://datasets/OpenSTEF/liander2024-stef-benchmark/profiles.parquet")

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Dataset Card for Liander 2024 Short Term Energy Forecasting Benchmark