segmentation_of_timeseries_with_tsam

Complexity

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0

Size/Duplication

Total Lines	94
Duplicated Lines	0 %

Importance

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0

import logging
import warnings
from pathlib import Path
import tsam.timeseriesaggregation as tsam

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from oemof.tools import debugging
from oemof.tools import logger

from oemof.solph import Bus
from oemof.solph import EnergySystem
from oemof.solph import Flow
from oemof.solph import Investment
from oemof.solph import Model
from oemof.solph import Results
from oemof.solph import components as cmp
from oemof import solph

warnings.filterwarnings(
    "ignore", category=debugging.ExperimentalFeatureWarning
)
logger.define_logging()

file_path = Path(__file__).parent

df = pd.read_csv(
    Path(file_path, "energy.csv"),
)
df["time"] = pd.to_datetime(df["Unix Epoch"], unit="s")
# time als Index setzen
df = df.set_index("time")
df = df.drop(columns=["Unix Epoch"])
print(df)

time_index = df.index

# Dummy pv profile
h = np.arange(len(time_index))
pv_profile = df["PV (W)"]

# Dummy electricity profile
df["house_elec_kW"] = 0.3 + 0.7 * np.random.rand(len(time_index))

# Dummy heat profile
df["house_heat_kW"] = 0.3 + 0.7 * np.random.rand(len(time_index))

# EV-Ladeprofil
df["ev_charge_kW"] = 0.0  # wird automatisch auf alle Zeitschritte gebroadcastet

# COP-Profil (konstant, später evtl. temperaturabhängig)
df["cop_hp"] = 3.5

#Clustering of Input time-series with TSAM
# not a high number of typical periods works with high number of hours per period
typical_periods = 7
hours_per_period = 24 * 60

aggregation_no_segmentation = tsam.TimeSeriesAggregation(
    timeSeries=df,
    noTypicalPeriods=typical_periods,
    hoursPerPeriod=hours_per_period,
    clusterMethod="k_means",
    sortValues=False,
    rescaleClusterPeriods=False,
)
aggregation_no_segmentation.createTypicalPeriods()
tindex_agg = pd.date_range(
    "2022-01-01", periods=typical_periods * hours_per_period , freq="H"
)

aggregation_no_segmentation.typicalPeriods["house_elec_kW"]

#aggregation with segmentation
# has to be hourly values, other values don't work because it is to big
df = df.resample("1h").mean()
typical_periods = 40
hours_per_period = 24

aggregation_with_segmentation = tsam.TimeSeriesAggregation(
    timeSeries=df,
    noTypicalPeriods=typical_periods,
    hoursPerPeriod=hours_per_period,
    clusterMethod="k_means",
    sortValues=False,
    rescaleClusterPeriods=False,
    segmentation=True,
    noSegments=6,
)
aggregation_with_segmentation.createTypicalPeriods()

aggregation_with_segmentation.typicalPeriods["house_elec_kW"]