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Temperature limit is part of the house data
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The temperature limit is connected to the efficiency status of the
house.
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@uvchik
uvchik committed Jun 21, 2022
1 parent 5d75001 commit 7cccf52
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Showing 3 changed files with 85 additions and 46 deletions.
10 changes: 5 additions & 5 deletions examples/vdi_profile_example.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,19 +43,19 @@


my_houses = []
for n in range(1):
for n in range(2):
my_houses.append(
{
"N_Pers": 3,
"name": "EFH_{0}".format(n),
"N_WE": 1,
"Q_Heiz_a": 6000,
"TRY": 4,
"copies": 24,
"house_type": "EFH",
"sigma": 4,
"Q_TWW_a": 1500,
"W_a": 5250,
"summer_temperature_limit": 15,
"winter_temperature_limit": 5,
}
)
my_houses.append(
Expand All @@ -64,12 +64,12 @@
"name": "MFH_{0}".format(n),
"N_WE": 15,
"Q_Heiz_a": 60000,
"TRY": 4,
"copies": 24,
"house_type": "MFH",
"sigma": 2,
"Q_TWW_a": 15000,
"W_a": 45000,
"summer_temperature_limit": 15,
"winter_temperature_limit": 5,
}
)

Expand Down
3 changes: 0 additions & 3 deletions src/demandlib/demandlib.ini
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Original file line number Diff line number Diff line change
@@ -1,3 +0,0 @@
[VDI]
summer_temperature_limit = 15
winter_temperature_limit = 5
118 changes: 80 additions & 38 deletions src/demandlib/vdi/regions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -52,10 +52,10 @@
import datetime
import os
import warnings
from collections import namedtuple

import pandas as pd

from demandlib import config as cfg
from demandlib.tools import add_weekdays2df
from demandlib.vdi import dwd_try

Expand Down Expand Up @@ -115,21 +115,43 @@ def __init__(

self._year = year
self.weather = None
self.type_days = self._get_typical_days(holidays)
self._load_profiles = self._load_profile_factors()

self.houses = []
if houses is not None:
self.add_houses(houses)

if resample_rule is not None:
self._resample_profiles(resample_rule)
self.temperature_limits = self._get_temperature_level_combinations()
self.type_days = {}
self._load_profiles = {}
for temp_limit in self.temperature_limits:
self.type_days[temp_limit] = self._get_typical_days(
holidays, temp_limit
)
self._load_profiles[temp_limit] = self._load_profile_factors(
temp_limit
)

if resample_rule is not None:
self._resample_profiles(resample_rule, temp_limit)

def _resample_profiles(self, rule):
self._load_profiles = self._load_profiles.resample(rule).sum()
self.type_days = self.type_days.resample(rule).first()
def _resample_profiles(self, rule, tl):
self._load_profiles[tl] = self._load_profiles[tl].resample(rule).sum()
self.type_days[tl] = self.type_days[tl].resample(rule).first()

def _get_temperature_level_combinations(self):
t_limit = namedtuple("temperature_limit", "summer winter")
return set(
[
t_limit(
summer=h["summer_temperature_limit"],
winter=h["winter_temperature_limit"],
)
for h in self.houses
]
)

def _get_typical_days(self, holidays, set_season="temperature"):
def _get_typical_days(
self, holidays, temperature_limit, set_season="temperature"
):
"""
Find the code for the typical days from dwd. The code consists of three
letters:
Expand Down Expand Up @@ -198,8 +220,8 @@ def _get_typical_days(self, holidays, set_season="temperature"):
"transition": "U",
}
days.replace(to_replace=seasons_dict, inplace=True)
wtl = cfg.get("VDI", "winter_temperature_limit")
stl = cfg.get("VDI", "summer_temperature_limit")
wtl = temperature_limit.winter
stl = temperature_limit.summer
days.loc[days["TAMB"] < wtl, "season_t"] = "W"
days.loc[days["TAMB"] >= wtl, "season_t"] = "U"
days.loc[days["TAMB"] > stl, "season_t"] = "S"
Expand Down Expand Up @@ -235,7 +257,7 @@ def _get_typical_days(self, holidays, set_season="temperature"):
)
return days

def _load_profile_factors(self):
def _load_profile_factors(self, tl):
"""Run VDI 4655 - Step 2.
Match 'typtag' keys and reference load profile factors for each
Expand Down Expand Up @@ -281,21 +303,21 @@ def _load_profile_factors(self):

# Set reference time (first hour of the day) to calculate minutes of
# the day.
self.type_days["ref_dt"] = self.type_days.index
self.type_days[tl]["ref_dt"] = self.type_days[tl].index

# Fill data into the large table with minute index
self.type_days = pd.concat(
[self.type_days, minute_table], axis=1
self.type_days[tl] = pd.concat(
[self.type_days[tl], minute_table], axis=1
).ffill()

# Add columns to merge with (house types, minute of day and day_types)
self.type_days["datetime"] = self.type_days.index
self.type_days["minute_of_day"] = (
self.type_days[tl]["datetime"] = self.type_days[tl].index
self.type_days[tl]["minute_of_day"] = (
pd.to_timedelta(
self.type_days.pop("datetime")
self.type_days[tl].pop("datetime")
- pd.Series(
index=self.type_days.index,
data=self.type_days.pop("ref_dt"),
index=self.type_days[tl].index,
data=self.type_days[tl].pop("ref_dt"),
)
)
.dt.total_seconds()
Expand All @@ -307,13 +329,17 @@ def _load_profile_factors(self):
# the typical days for every minute of the year.
house_profiles = {}
for house_type in house_types:
self.type_days["ht"] = house_type
house_profiles[house_type] = self.type_days.merge(
typtage_df,
how="left",
left_on=["day_types", "minute_of_day", "ht"],
right_on=["day_types", "minute_of_day", "ht"],
).sort_index()
self.type_days[tl]["ht"] = house_type
house_profiles[house_type] = (
self.type_days[tl]
.merge(
typtage_df,
how="left",
left_on=["day_types", "minute_of_day", "ht"],
right_on=["day_types", "minute_of_day", "ht"],
)
.sort_index()
)

# Combine the table of both house type
load_profile = (
Expand Down Expand Up @@ -359,7 +385,7 @@ def add_houses(self, houses):
if len(houses) > 0:
self.houses.extend(houses)

def get_daily_energy_demand_houses(self):
def get_daily_energy_demand_houses(self, tl):
"""Determine the houses' energy demand values for each 'typtag'.
.. note::
Expand Down Expand Up @@ -407,7 +433,7 @@ def get_daily_energy_demand_houses(self):
multiindex = pd.MultiIndex.from_product(
iterables, names=["house", "energy"]
)
typtage_combinations = self.type_days["day_types"].unique()
typtage_combinations = self.type_days[tl]["day_types"].unique()

daily_energy_demand_houses = pd.DataFrame(
index=multiindex, columns=typtage_combinations
Expand Down Expand Up @@ -507,21 +533,34 @@ def get_load_curve_houses(self):
value with the daily energy demand. It returns the result: the energy
demand values for all houses and energy types (in kWh)
"""
daily_energy_demand_houses = self.get_daily_energy_demand_houses()
daily_energy_demand_houses = {}
for temp_limit in self.temperature_limits:
daily_energy_demand_houses[
temp_limit
] = self.get_daily_energy_demand_houses(temp_limit)

house_profiles = {}

for house in self.houses:
df_typ = self.type_days.merge(
daily_energy_demand_houses.T[house["name"]],
left_on="day_types",
right_index=True,
).sort_index()
t_limit = namedtuple("temperature_limit", "summer winter")
tl = t_limit(
summer=house["summer_temperature_limit"],
winter=house["winter_temperature_limit"],
)
df_typ = (
self.type_days[tl]
.merge(
daily_energy_demand_houses[tl].T[house["name"]],
left_on="day_types",
right_index=True,
)
.sort_index()
)
df_typ.drop(
["day_types", "minute_of_day", "ht"], axis=1, inplace=True
)

load_profile_df = self._load_profiles.rename(
load_profile_df = self._load_profiles[tl].rename(
columns={
"F_Heiz_n_TT": "Q_Heiz_TT",
"F_el_n_TT": "W_TT",
Expand Down Expand Up @@ -551,6 +590,9 @@ def get_load_curve_houses(self):
]
)
house_profiles[house["name"]] = load_curve_house
return pd.concat(
df = pd.concat(
house_profiles.values(), keys=house_profiles.keys(), axis=1
)

df.columns = df.columns.set_names(["name", "house_type", "energy"])
return df

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