70 lines
2.0 KiB
Python
70 lines
2.0 KiB
Python
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# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2020-2023 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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"""
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Build hourly heat demand time series from daily ones.
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"""
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import pandas as pd
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import xarray as xr
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from _helpers import generate_periodic_profiles, update_config_with_sector_opts
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from itertools import product
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if __name__ == "__main__":
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if "snakemake" not in globals():
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from _helpers import mock_snakemake
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snakemake = mock_snakemake(
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"build_heat_demands",
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simpl="",
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clusters=48,
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)
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snapshots = pd.date_range(freq="h", **snakemake.params.snapshots)
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daily_space_heat_demand = (
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xr.open_dataarray(snakemake.input.heat_demand)
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.to_pandas()
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.reindex(index=snapshots, method="ffill")
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)
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intraday_profiles = pd.read_csv(snakemake.input.heat_profile, index_col=0)
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sectors = ["residential", "services"]
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uses = ["water", "space"]
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heat_demand = {}
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for sector, use in product(sectors, uses):
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weekday = list(intraday_profiles[f"{sector} {use} weekday"])
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weekend = list(intraday_profiles[f"{sector} {use} weekend"])
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weekly_profile = weekday * 5 + weekend * 2
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intraday_year_profile = generate_periodic_profiles(
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daily_space_heat_demand.index.tz_localize("UTC"),
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nodes=daily_space_heat_demand.columns,
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weekly_profile=weekly_profile,
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)
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if use == "space":
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heat_demand[f"{sector} {use}"] = daily_space_heat_demand * intraday_year_profile
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else:
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heat_demand[f"{sector} {use}"] = intraday_year_profile
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heat_demand = pd.concat(heat_demand,
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axis=1,
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names = ["sector use", "node"])
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heat_demand.index.name="snapshots"
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print(heat_demand)
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print(heat_demand.stack())
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ds = heat_demand.stack().to_xarray()#xr.Dataset.from_dataframe(heat_demand)
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print(ds)
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ds.to_netcdf(snakemake.output.heat_demand)
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