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Merge branch 'master' into harmonize_clustering_strategies

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Martha Frysztacki 2022-06-24 20:10:05 +02:00 committed by GitHub
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1 changed files with 17 additions and 6 deletions
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21
scripts/build_load_data.py
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@ -116,14 +116,19 @@ def nan_statistics(df):
keys=['total', 'consecutive', 'max_total_per_month'], axis=1) keys=['total', 'consecutive', 'max_total_per_month'], axis=1)
def copy_timeslice(load, cntry, start, stop, delta): def copy_timeslice(load, cntry, start, stop, delta, fn_load=None):
start = pd.Timestamp(start) start = pd.Timestamp(start)
stop = pd.Timestamp(stop) stop = pd.Timestamp(stop)
if start-delta in load.index and stop in load.index and cntry in load: 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 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], powerstatistics)
load.loc[start:stop, cntry] = load_raw.loc[start-delta:stop-delta, cntry].values
def manual_adjustment(load, powerstatistics): def manual_adjustment(load, fn_load, powerstatistics):
""" """
Adjust gaps manual for load data from OPSD time-series package. Adjust gaps manual for load data from OPSD time-series package.
@ -150,6 +155,8 @@ def manual_adjustment(load, powerstatistics):
powerstatistics: bool powerstatistics: bool
Whether argument load comprises the electricity consumption data of Whether argument load comprises the electricity consumption data of
the ENTSOE power statistics or of the ENTSOE transparency map the ENTSOE power statistics or of the ENTSOE transparency map
load_fn: str
File name or url location (file format .csv)
Returns Returns
------- -------
@ -175,7 +182,11 @@ def manual_adjustment(load, powerstatistics):
copy_timeslice(load, 'CH', '2010-11-04 04:00', '2010-11-04 22:00', Delta(days=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)) copy_timeslice(load, 'NO', '2010-12-09 11:00', '2010-12-09 18:00', Delta(days=1))
# whole january missing # whole january missing
copy_timeslice(load, 'GB', '2009-12-31 23:00', '2010-01-31 23:00', Delta(days=-364)) 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)
else: else:
if 'ME' in load: if 'ME' in load:
@ -206,7 +217,7 @@ if __name__ == "__main__":
load = load_timeseries(snakemake.input[0], years, countries, powerstatistics) load = load_timeseries(snakemake.input[0], years, countries, powerstatistics)
if snakemake.config['load']['manual_adjustments']: if snakemake.config['load']['manual_adjustments']:
load = manual_adjustment(load, powerstatistics) load = manual_adjustment(load, snakemake.input[0], powerstatistics)
logger.info(f"Linearly interpolate gaps of size {interpolate_limit} and less.") logger.info(f"Linearly interpolate gaps of size {interpolate_limit} and less.")
load = load.interpolate(method='linear', limit=interpolate_limit) load = load.interpolate(method='linear', limit=interpolate_limit)