example_midterm_predictions: Example Midterm Predictions Data

In oRaklE: Multi-Horizon Electricity Demand Forecasting in High Resolution

Example Midterm Predictions Data

Description

This dataset extends the demand dataframe from example_midterm_demand_and_weather_data with the prediction results from the best derived mid-term seasonality model. It also includes all used covariates for the model selection process.

Usage

example_midterm_predictions

Format

A data frame with 1,825 rows and 46 columns:

country

The country, represented by the ISO2C country code (e.g., FR for France).

date

The date (in YYYY-MM-DD format).

year

The respective year.

month

The respective month.

day

The respective day.

wday

The type of weekday (e.g., Sun, Mon)

avg_hourly_demand

The average hourly electricity demand (in megawatts) for the day.

seasonal_avg_hourly_demand

The seasonal average hourly demand (in megawatts) for the day.

holiday

Indicates whether the day is a public holiday (1 for holiday, 0 for non-holiday).

weighted_temperature

The weighted average temperature for France on that day (in degrees Celsius).

Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Nov, Dec

Monthly dummy variables for January through December, indicating the respective month (1 if the date belongs to the month, 0 otherwise).

Sun, Mon, Tue, Wed, Thu, Fri, Sat

Weekly dummy variables for Sunday through Saturday, indicating the respective weekday (1 if the date is the specific weekday, 0 otherwise).

HD

The weighted temperature converted to heating degree days.

CD

The weighted temperature converted to cooling degree days.

HD2

The squared heating degree days (HD).

HD3

The cubed heating degree days (HD).

CD2

The squared cooling degree days (CD).

CD3

The cubed cooling degree days (CD).

weighted_temperature2

The squared weighted temperature.

weighted_temperature3

The cubed weighted temperature.

HDlag1

Lagged value of heating degree days (1 day).

HDlag2

Lagged value of heating degree days (2 days).

CDlag1

Lagged value of cooling degree days (1 day).

CDlag2

Lagged value of cooling degree days (2 days).

weighted_temperaturelag1

Lagged weighted temperature (1 day).

weighted_temperaturelag2

Lagged weighted temperature (2 days).

midterm_model_fit

model predictions for the seasonal mid-term component.

end_of_year

Binary dummy variable to account for lower demand between Christmas and New Year's Evening. Starts at 22nd December.

test_set_steps

Number of days used in the test set for model evaluation.

example

A boolean indicator to mark this dataset as an example dataset.

Source

demand data: Transparency Platform of the European Network of Transmission System Operators for Electricity (ENTSO-E, https://transparency.entsoe.eu/); holidays: https://date.nager.at/api/v3/publicholidays/ ; area population: https://wft-geo-db.p.rapidapi.com ; daily average temperatures: https://meteostat.p.rapidapi.com;

oRaklE documentation built on June 8, 2025, 12:41 p.m.