Feature engineering for electricity load forecasting

The purpose of this notebook is to demonstrate how to use skrub and polars to perform feature engineering for electricity load forecasting.

We will build a set of features (and targets) from different data sources:

• Historical weather data for 10 medium to large urban areas in France;

• Holidays and standard calendar features for France;

• Historical electricity load data for the whole of France.

All these data sources cover a time range from March 23, 2021 to May 31, 2025.

Since our forecasting horizon is 24 hours, we consider that the future weather data is known at a chosen prediction time. Similarly, the holidays and calendar features are known at prediction time for any point in the future. We can also use the load data to engineer some lagged features and rolling aggregations.

The future values of the load data (with respect to the prediction time) are used as targets for the forecasting model.

Environment setup

We need to install some extra dependencies for this notebook if needed (when running jupyterlite).

%pip install -q https://pypi.anaconda.org/ogrisel/simple/polars/1.24.0/polars-1.24.0-cp39-abi3-emscripten_3_1_58_wasm32.whl
%pip install -q altair holidays plotly nbformat skrub

ERROR: polars-1.24.0-cp39-abi3-emscripten_3_1_58_wasm32.whl is not a supported wheel on this platform.

Note: you may need to restart the kernel to use updated packages.

Note: you may need to restart the kernel to use updated packages.

The following 3 imports are only needed to workaround some limitations when using polars in a pyodide/jupyterlite notebook.

import tzdata  # noqa: F401
import pandas as pd
from pyarrow.parquet import read_table

import altair
import polars as pl
import skrub
from pathlib import Path
import holidays

Shared time range for all historical data sources

Let’s define a hourly time range from March 23, 2021 to May 31, 2025 that will be used to join the electricity load data and the weather data. The time range is in UTC timezone to avoid any ambiguity when joining with the weather data that is also in UTC.

We wrap the resulting polars dataframe in a skrub DataOp to benefit from the built-in skrub.TableReport display in the notebook. Using the skrub DataOps will also be useful for other reasons: all operations in this notebook are chained together in a directed acyclic graph that is automatically tracked by skrub. This allows us to extract the resulting pipeline and apply it to new data later on, exactly like a trained scikit-learn pipeline. The main difference is that we do so incrementally and while eagerly executing and inspecting the results of each step as is customary when working with dataframe libraries such as polars and pandas in Jupyter notebooks.

historical_data_start_time = skrub.var(
    "historical_data_start_time", pl.datetime(2021, 3, 23, hour=0, time_zone="UTC")
)
historical_data_end_time = skrub.var(
    "historical_data_end_time", pl.datetime(2025, 5, 31, hour=23, time_zone="UTC")
)

@skrub.deferred
def build_historical_time_range(
    historical_data_start_time,
    historical_data_end_time,
    time_interval="1h",
    time_zone="UTC",
):
    """Define an historical time range shared by all data sources."""
    return pl.DataFrame().with_columns(
        pl.datetime_range(
            start=historical_data_start_time,
            end=historical_data_end_time,
            time_zone=time_zone,
            interval=time_interval,
        ).alias("time"),
    )


time = build_historical_time_range(historical_data_start_time, historical_data_end_time)
time

<Call 'build_historical_time_range'>

Show graph

Result:

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If you run the above locally with pydot and graphviz installed, you can visualize the expression graph of the time variable by expanding the “Show graph” button.

Let’s now load the data records for the time range defined above.

To avoid network issues when running this notebook, the necessary data files have already been downloaded and saved in the datasets folder. See the README.md file for instructions to download the data manually if you want to re-run this notebook with more recent data.

data_source_folder = skrub.var("data_source_folder", "../datasets")

for data_file in sorted(Path(data_source_folder.skb.eval()).iterdir()):
    print(data_file)

../datasets/README.md
../datasets/Total Load - Day Ahead _ Actual_202101010000-202201010000.csv
../datasets/Total Load - Day Ahead _ Actual_202201010000-202301010000.csv
../datasets/Total Load - Day Ahead _ Actual_202301010000-202401010000.csv
../datasets/Total Load - Day Ahead _ Actual_202401010000-202501010000.csv
../datasets/Total Load - Day Ahead _ Actual_202501010000-202601010000.csv
../datasets/weather_bayonne.parquet
../datasets/weather_brest.parquet
../datasets/weather_lille.parquet
../datasets/weather_limoges.parquet
../datasets/weather_lyon.parquet
../datasets/weather_marseille.parquet
../datasets/weather_nantes.parquet
../datasets/weather_paris.parquet
../datasets/weather_strasbourg.parquet
../datasets/weather_toulouse.parquet

We define a list of 10 medium to large urban areas to approximately cover most regions in France with a slight focus on most populated regions that are likely to drive electricity demand.

city_names = skrub.var(
    "city_names",
    [
        "paris",
        "lyon",
        "marseille",
        "toulouse",
        "lille",
        "limoges",
        "nantes",
        "strasbourg",
        "brest",
        "bayonne",
    ],
)


@skrub.deferred
def load_weather_data(time, city_names, data_source_folder):
    """Load and horizontal stack historical weather forecast data for each city."""
    all_city_weather = time
    for city_name in city_names:
        all_city_weather = all_city_weather.join(
            pl.from_arrow(
                read_table(f"{data_source_folder}/weather_{city_name}.parquet")
            )
            .with_columns([pl.col("time").dt.cast_time_unit("us")])
            .rename(lambda x: x if x == "time" else "weather_" + x + "_" + city_name),
            on="time",
        )
    return all_city_weather


all_city_weather = load_weather_data(time, city_names, data_source_folder)
all_city_weather

<Call 'load_weather_data'>

Show graph

Result:

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Calendar and holidays features

We leverage the holidays package to enrich the time range with some calendar features such as public holidays in France. We also add some features that are useful for time series forecasting such as the day of the week, the day of the year, and the hour of the day.

We want to use the holidays package to enrich the time range with some calendar features such as public holidays in France. In addition, we want to use skrub’s DatetimeEncoder to add some features that are useful for time series forecasting such as the calendar year, month, day, hour, the day of the week and the day of the year.

Note that the holidays package requires us to extract the date for the French timezone.

Similarly for the calendar features: all the time features are extracted from the time in the French timezone, since it is likely that electricity usage patterns are influenced by inhabitants’ daily routines aligned with the local timezone.

Exercise

Let’s first create some calendar features using skrub’s DatetimeEncoder.

1. Create a DatetimeEncoder object and, by looking at the documentation, make sure to add the weekday and the day of the year. Do not add the total seconds since the Unix epoch. You can refer to this link: https://skrub-data.org/stable/reference/generated/skrub.DatetimeEncoder.html

2. As a first operation, we wish to rename the time column to cal such that all the columns corresponding to some calendar features will be prefixed with cal_. You can simply call the rename method (cf. https://docs.pola.rs/api/python/stable/reference/dataframe/api/polars.DataFrame.rename.html) because time can be seen as a polars dataframe.

3. Now, we wish to apply the encoder to the time dataframe. Refer to the following link for all details: https://skrub-data.org/stable/reference/generated/skrub.DataOp.skb.apply.html

4. Let’s call the resulting skrub DataOp time_encoded and check the output representation to check if the preview looks what we expect.

from skrub import DatetimeEncoder

Solution

datetime_encoder = DatetimeEncoder(
    add_weekday=True, add_day_of_year=True, add_total_seconds=False
)
time_encoded = time.rename({"time": "cal"}).skb.apply(datetime_encoder)
time_encoded

<Apply DatetimeEncoder>

Show graph

Result:

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Exercise

Now, let’s create a processing function that is going to be decorated with the @skrub.deferred decorator. This function should:

1. Take the time dataframe as an input.

2. Convert the “time” column to the French/Paris timezone.

3. Extract the French holidays by calling holidays.country_holidays. For this function, you need to extract the minimum and maximum year from the “time” column.

4. Finally, you need to add if a date in holiday is a French holiday as a feature. You can call this column cal_is_holiday.

5. Apply this function to the time DataOp and call the resulting variable is_french_holiday.

6. Finally, we wish to concatenate the time_encoded and is_french_holiday DataOpsusing the .skb.concat method.

Solution

@skrub.deferred
def prepare_holidays(time):
    fr_time = pl.col("time").dt.convert_time_zone("Europe/Paris")
    fr_year_min = time.select(fr_time.dt.year().min()).item()
    fr_year_max = time.select(fr_time.dt.year().max()).item()
    holidays_fr = holidays.country_holidays(
        "FR", years=range(fr_year_min, fr_year_max + 1)
    )
    return time.select(
        fr_time.dt.date().is_in(holidays_fr.keys()).alias("cal_is_holiday"),
    )

is_french_holiday = prepare_holidays(time)
is_french_holiday

calendar = time.skb.concat([time_encoded, is_french_holiday], axis=1)
calendar

<Concat: 3 dataframes>

Show graph

Result:

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Electricity load data

Finally, we load the electricity load data. This data will both be used as a target variable but also to craft some lagged and window-aggregated features.

@skrub.deferred
def load_electricity_load_data(time, data_source_folder):
    """Load and aggregate historical load data from the raw CSV files."""
    load_data_files = [
        data_file
        for data_file in sorted(Path(data_source_folder).iterdir())
        if data_file.name.startswith("Total Load - Day Ahead")
        and data_file.name.endswith(".csv")
    ]
    return time.join(
        (
            pl.concat(
                [
                    pl.from_pandas(pd.read_csv(data_file, na_values=["N/A", "-"])).drop(
                        ["Day-ahead Total Load Forecast [MW] - BZN|FR"]
                    )
                    for data_file in load_data_files
                ]
            ).select(
                [
                    pl.col("Time (UTC)")
                    .str.split(by=" - ")
                    .list.first()
                    .str.to_datetime("%d.%m.%Y %H:%M", time_zone="UTC")
                    .alias("time"),
                    pl.col("Actual Total Load [MW] - BZN|FR").alias("load_mw"),
                ]
            )
        ),
        on="time",
    )

Let’s load the data and check if there are missing values since we will use this data as the target variable for our forecasting model.

electricity_raw = load_electricity_load_data(time, data_source_folder)
electricity_raw.filter(pl.col("load_mw").is_null())

<CallMethod 'filter'>

Show graph

Result:

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So apparently there are a few missing measurements. Let’s use linear interpolation to fill those missing values.

electricity_raw.filter(
    (pl.col("time") > pl.datetime(2021, 10, 30, hour=10, time_zone="UTC"))
    & (pl.col("time") < pl.datetime(2021, 10, 31, hour=10, time_zone="UTC"))
).skb.eval().plot.line(x="time:T", y="load_mw:Q")

electricity = electricity_raw.with_columns([pl.col("load_mw").interpolate()])
electricity.filter(
    (pl.col("time") > pl.datetime(2021, 10, 30, hour=10, time_zone="UTC"))
    & (pl.col("time") < pl.datetime(2021, 10, 31, hour=10, time_zone="UTC"))
).skb.eval().plot.line(x="time:T", y="load_mw:Q")

Lagged features

We can now create some lagged features from the electricity load data.

We will create 3 hourly lagged features, 1 daily lagged feature, and 1 weekly lagged feature. We will also create a rolling median and inter-quartile feature over the last 24 hours and over the last 7 days. Inter-quartile features tell us what is the variability of the load over the given window.

def iqr(col, *, window_size: int):
    """Inter-quartile range (IQR) of a column."""
    return col.rolling_quantile(0.75, window_size=window_size) - col.rolling_quantile(
        0.25, window_size=window_size
    )


electricity_lagged = electricity.with_columns(
    [pl.col("load_mw").shift(i).alias(f"load_mw_lag_{i}h") for i in range(1, 4)]
    + [
        pl.col("load_mw").shift(24).alias("load_mw_lag_1d"),
        pl.col("load_mw").shift(24 * 7).alias("load_mw_lag_1w"),
        pl.col("load_mw")
        .rolling_median(window_size=24)
        .alias("load_mw_rolling_median_24h"),
        pl.col("load_mw")
        .rolling_median(window_size=24 * 7)
        .alias("load_mw_rolling_median_7d"),
        iqr(pl.col("load_mw"), window_size=24).alias("load_mw_iqr_24h"),
        iqr(pl.col("load_mw"), window_size=24 * 7).alias("load_mw_iqr_7d"),
    ],
)
electricity_lagged

<CallMethod 'with_columns'>

Show graph

Result:

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altair.Chart(electricity_lagged.tail(100).skb.preview()).transform_fold(
    [
        "load_mw",
        "load_mw_lag_1h",
        "load_mw_lag_2h",
        "load_mw_lag_3h",
        "load_mw_lag_1d",
        "load_mw_lag_1w",
        "load_mw_rolling_median_24h",
        "load_mw_rolling_median_7d",
        "load_mw_rolling_iqr_24h",
        "load_mw_rolling_iqr_7d",
    ],
    as_=["key", "load_mw"],
).mark_line(tooltip=True).encode(x="time:T", y="load_mw:Q", color="key:N").interactive()

Final dataset

We now assemble the dataset that will be used to train and evaluate the forecasting models via backtesting.

prediction_start_time = skrub.var(
    "prediction_start_time", historical_data_start_time.skb.eval() + pl.duration(days=7)
)
prediction_end_time = skrub.var(
    "prediction_end_time", historical_data_end_time.skb.eval() - pl.duration(hours=24)
)


@skrub.deferred
def define_prediction_time_range(prediction_start_time, prediction_end_time):
    return pl.DataFrame().with_columns(
        pl.datetime_range(
            start=prediction_start_time,
            end=prediction_end_time,
            time_zone="UTC",
            interval="1h",
        ).alias("prediction_time"),
    )


prediction_time = define_prediction_time_range(
    prediction_start_time, prediction_end_time
).skb.subsample(n=1000, how="head")
prediction_time

<SubsamplePreviews>

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Result (on a subsample):

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@skrub.deferred
def build_features(
    prediction_time,
    electricity_lagged,
    all_city_weather,
    calendar,
    future_feature_horizons=[1, 24],
):

    return (
        prediction_time.join(
            electricity_lagged, left_on="prediction_time", right_on="time"
        )
        .join(
            all_city_weather.select(
                [pl.col("time")]
                + [
                    pl.col(c).shift(-h).alias(c + f"_future_{h}h")
                    for c in all_city_weather.columns
                    if c != "time"
                    for h in future_feature_horizons
                ]
            ),
            left_on="prediction_time",
            right_on="time",
        )
        .join(
            calendar.select(
                [pl.col("time")]
                + [
                    pl.col(c).shift(-h).alias(c + f"_future_{h}h")
                    for c in calendar.columns
                    if c != "time"
                    for h in future_feature_horizons
                ]
            ),
            left_on="prediction_time",
            right_on="time",
        )
    ).drop("prediction_time")


features = build_features(
    prediction_time=prediction_time,
    electricity_lagged=electricity_lagged,
    all_city_weather=all_city_weather,
    calendar=calendar,
).skb.mark_as_X()

features

<Call 'build_features'>

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Result (on a subsample):

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Let’s build training and evaluation targets for all possible horizons from 1 to 24 hours.

@skrub.deferred
def build_targets(prediction_time, electricity):
    return prediction_time.join(
        electricity.with_columns(
            pl.col("load_mw").shift(-24).alias("load_mw_horizon_24h")
        ),
        left_on="prediction_time",
        right_on="time",
    )


targets = build_targets(prediction_time, electricity)
targets

<Call 'build_targets'>

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Result (on a subsample):

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Let’s serialize this data loading and feature engineering pipeline for reuse in later notebooks.

import cloudpickle

with open("feature_engineering_pipeline.pkl", "wb") as f:
    cloudpickle.dump(
        {
            "features": features,
            "targets": targets,
            "prediction_time": prediction_time,
        },
        f,
    )