# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2020 @JanFrederickUnnewehr, 2020-2024 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
This rule downloads the load data from `Open Power System Data Time series
<https://data.open-power-system-data.org/time_series/>`_. For all countries in
the network, the per country load timeseries are extracted from the dataset.
After filling small gaps linearly and large gaps by copying time-slice of a
given period, the load data is exported to a ``.csv`` file.
Relevant Settings
-----------------
.. code:: yaml
snapshots:
load:
interpolate_limit: time_shift_for_large_gaps: manual_adjustments:
.. seealso::
Documentation of the configuration file ``config/config.yaml`` at
:ref:`load_cf`
Inputs
------
- ``data/electricity_demand_raw.csv``:
Outputs
-------
- ``resources/electricity_demand.csv``:
"""
import logging
import numpy as np
import pandas as pd
from _helpers import configure_logging, get_snapshots, set_scenario_config
from pandas import Timedelta as Delta
logger = logging.getLogger(__name__)
def load_timeseries(fn, years, countries):
"""
Read load data from OPSD time-series package version 2020-10-06.
Parameters
----------
years : None or slice()
Years for which to read load data (defaults to
slice("2018","2019"))
fn : str
File name or url location (file format .csv)
countries : listlike
Countries for which to read load data.
Returns
-------
load : pd.DataFrame
Load time-series with UTC timestamps x ISO-2 countries
"""
return (
pd.read_csv(fn, index_col=0, parse_dates=[0], date_format="%Y-%m-%dT%H:%M:%SZ")
.tz_localize(None)
.dropna(how="all", axis=0)
.rename(columns={"GB_UKM": "GB"})
.filter(items=countries)
.loc[years]
)
def consecutive_nans(ds):
return (
ds.isnull()
.astype(int)
.groupby(ds.notnull().astype(int).cumsum()[ds.isnull()])
.transform("sum")
.fillna(0)
)
def fill_large_gaps(ds, shift):
"""
Fill up large gaps with load data from the previous week.
This function fills gaps ragning from 3 to 168 hours (one week).
"""
shift = Delta(shift)
nhours = shift / np.timedelta64(1, "h")
if (consecutive_nans(ds) > nhours).any():
logger.warning(
"There exist gaps larger then the time shift used for "
"copying time slices."
)
time_shift = pd.Series(ds.values, ds.index + shift)
return ds.where(ds.notnull(), time_shift.reindex_like(ds))
def nan_statistics(df):
def max_consecutive_nans(ds):
return (
ds.isnull()
.astype(int)
.groupby(ds.notnull().astype(int).cumsum())
.sum()
.max()
)
consecutive = df.apply(max_consecutive_nans)
total = df.isnull().sum()
max_total_per_month = df.isnull().resample("m").sum().max()
return pd.concat(
[total, consecutive, max_total_per_month],
keys=["total", "consecutive", "max_total_per_month"],
axis=1,
)
def copy_timeslice(load, cntry, start, stop, delta, fn_load=None):
start = pd.Timestamp(start)
stop = pd.Timestamp(stop)
if start in load.index and stop in load.index:
if start - delta in load.index and stop - delta in load.index and cntry in load:
load.loc[start:stop, cntry] = load.loc[
start - delta : stop - delta, cntry
].values
elif fn_load is not None:
duration = pd.date_range(freq="h", start=start - delta, end=stop - delta)
load_raw = load_timeseries(fn_load, duration, [cntry])
load.loc[start:stop, cntry] = load_raw.loc[
start - delta : stop - delta, cntry
].values
def manual_adjustment(load, fn_load, countries):
"""
Adjust gaps manual for load data from OPSD time-series package.
1. For years later than 2015 for which the load data is mainly taken from the
ENTSOE power statistics
Kosovo (KV) and Albania (AL) do not exist in the data set. Kosovo gets the
same load curve as Serbia and Albania the same as Macdedonia, both scaled
by the corresponding ratio of total energy consumptions reported by
IEA Data browser [0] for the year 2013.
2. For years earlier than 2015 for which the load data is mainly taken from the
ENTSOE transparency platforms
Albania (AL) and Macedonia (MK) do not exist in the data set. Both get the
same load curve as Montenegro, scaled by the corresponding ratio of total energy
consumptions reported by IEA Data browser [0] for the year 2016.
[0] https://www.iea.org/data-and-statistics?country=WORLD&fuel=Electricity%20and%20heat&indicator=TotElecCons
Bosnia and Herzegovina (BA) does not exist in the data set for 2019. It gets the
electricity consumption data from Croatia (HR) for the year 2019, scaled by the
factors derived from https://energy.at-site.be/eurostat-2021/
Parameters
----------
load : pd.DataFrame
Load time-series with UTC timestamps x ISO-2 countries
load_fn: str
File name or url location (file format .csv)
Returns
-------
load : pd.DataFrame
Manual adjusted and interpolated load time-series with UTC
timestamps x ISO-2 countries
"""
if "AL" not in load and "AL" in countries:
if "ME" in load:
load["AL"] = load.ME * (5.7 / 2.9)
elif "MK" in load:
load["AL"] = load["MK"] * (4.1 / 7.4)
if "MK" in countries and "MK" in countries:
if "MK" not in load or load.MK.isnull().sum() > len(load) / 2:
if "ME" in load:
load["MK"] = load.ME * (6.7 / 2.9)
if "BA" not in load and "BA" in countries:
if "ME" in load:
load["BA"] = load.HR * (11.0 / 16.2)
if ("KV" not in load or load.KV.isnull().values.all()) and "KV" in countries:
if "RS" in load:
load["KV"] = load["RS"] * (4.8 / 27.0)
copy_timeslice(load, "GR", "2015-08-11 21:00", "2015-08-15 20:00", Delta(weeks=1))
copy_timeslice(load, "AT", "2018-12-31 22:00", "2019-01-01 22:00", Delta(days=2))
copy_timeslice(load, "CH", "2010-01-19 07:00", "2010-01-19 22:00", Delta(days=1))
copy_timeslice(load, "CH", "2010-03-28 00:00", "2010-03-28 21:00", Delta(days=1))
# is a WE, so take WE before
copy_timeslice(load, "CH", "2010-10-08 13:00", "2010-10-10 21:00", Delta(weeks=1))
copy_timeslice(load, "CH", "2010-11-04 04:00", "2010-11-04 22:00", Delta(days=1))
copy_timeslice(load, "NO", "2010-12-09 11:00", "2010-12-09 18:00", Delta(days=1))
# whole january missing
copy_timeslice(
load,
"GB",
"2010-01-01 00:00",
"2010-01-31 23:00",
Delta(days=-365),
fn_load,
)
# 1.1. at midnight gets special treatment
copy_timeslice(
load,
"IE",
"2016-01-01 00:00",
"2016-01-01 01:00",
Delta(days=-366),
fn_load,
)
copy_timeslice(
load,
"PT",
"2016-01-01 00:00",
"2016-01-01 01:00",
Delta(days=-366),
fn_load,
)
copy_timeslice(
load,
"GB",
"2016-01-01 00:00",
"2016-01-01 01:00",
Delta(days=-366),
fn_load,
)
copy_timeslice(load, "BG", "2018-10-27 21:00", "2018-10-28 22:00", Delta(weeks=1))
copy_timeslice(load, "LU", "2019-01-02 11:00", "2019-01-05 05:00", Delta(weeks=-1))
copy_timeslice(load, "LU", "2019-02-05 20:00", "2019-02-06 19:00", Delta(weeks=-1))
if "UA" in countries:
copy_timeslice(
load, "UA", "2013-01-25 14:00", "2013-01-28 21:00", Delta(weeks=1)
)
copy_timeslice(
load, "UA", "2013-10-28 03:00", "2013-10-28 20:00", Delta(weeks=1)
)
return load
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake("build_electricity_demand")
configure_logging(snakemake)
set_scenario_config(snakemake)
snapshots = get_snapshots(
snakemake.params.snapshots, snakemake.params.drop_leap_day
)
fixed_year = snakemake.params["load"].get("fixed_year", False)
years = (
slice(str(fixed_year), str(fixed_year))
if fixed_year
else slice(snapshots[0], snapshots[-1])
)
interpolate_limit = snakemake.params.load["interpolate_limit"]
countries = snakemake.params.countries
time_shift = snakemake.params.load["time_shift_for_large_gaps"]
load = load_timeseries(snakemake.input.reported, years, countries)
load = load.reindex(index=snapshots)
if "UA" in countries:
# attach load of UA (best data only for entsoe transparency)
load_ua = load_timeseries(snakemake.input.reported, "2018", ["UA"])
snapshot_year = str(snapshots.year.unique().item())
time_diff = pd.Timestamp("2018") - pd.Timestamp(snapshot_year)
# hack indices (currently, UA is manually set to 2018)
load_ua.index -= time_diff
load["UA"] = load_ua
# attach load of MD (no time-series available, use 2020-totals and distribute according to UA):
# https://www.iea.org/data-and-statistics/data-browser/?country=MOLDOVA&fuel=Energy%20consumption&indicator=TotElecCons
if "MD" in countries:
load["MD"] = 6.2e6 * (load_ua / load_ua.sum())
if snakemake.params.load["manual_adjustments"]:
load = manual_adjustment(load, snakemake.input[0], countries)
logger.info(f"Linearly interpolate gaps of size {interpolate_limit} and less.")
load = load.interpolate(method="linear", limit=interpolate_limit)
logger.info(
"Filling larger gaps by copying time-slices of period " f"'{time_shift}'."
)
load = load.apply(fill_large_gaps, shift=time_shift)
if snakemake.params.load["supplement_synthetic"]:
logger.info("Supplement missing data with synthetic data.")
fn = snakemake.input.synthetic
synthetic_load = pd.read_csv(fn, index_col=0, parse_dates=True)
synthetic_load = synthetic_load.loc[snapshots, countries]
load = load.combine_first(synthetic_load)
assert not load.isna().any().any(), (
"Load data contains nans. Adjust the parameters "
"`time_shift_for_large_gaps` or modify the `manual_adjustment` function "
"for implementing the needed load data modifications."
)
# need to reindex load time series to target year
if fixed_year:
load.index = load.index.map(lambda t: t.replace(year=snapshots.year[0]))
load.to_csv(snakemake.output[0])