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automate_studies.py
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import pandas as pd
import os
import pathlib
import logging
import shutil
import itertools
from ebcpy import DymolaAPI, TimeSeriesData
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.lines import Line2D
from matplotlib.patches import Patch
from dataclasses import dataclass
#plt.style.use("seaborn")
plt.rcParams.update({"figure.figsize": [15, 8],
"font.size": 14})
logger = logging.getLogger(__name__)
logger.setLevel("DEBUG")
from copy import deepcopy
def get_dhw_size(
profile: str = "M",
heat_loss_per_day: float = 1.0,
temperature_set: float = 50,
):
"""
V_S in m3
TSet in °C
TCold in °C
"""
profile_data = {
"M": {
"V_S": 123.417e-3,
"t_DP": 1,
"Q_DP": 2.24
}
}
Q_Loss = heat_loss_per_day / 24 # From kWh/d to kWh/h=kW
_data = profile_data[profile]
Q_DP = _data["Q_DP"]
t_DP = _data["t_DP"]
V_S = _data["V_S"]
return ((Q_DP - (V_S * 4184 * 1000 / 3600000) * (temperature_set - 40)) / t_DP + Q_Loss) * 1000
_KWH_PLOTS = {"factor": 1/36e5, "offset": 0, "unit": "kWh"}
_N_PLOTS = {"factor": 1, "offset": 0, "unit": "-"}
_KH_PLOTS = {"factor": 1 / 3600, "offset": 0, "unit": "Kh"}
_KH2_PLOTS = {"factor": 1 / 3600, "offset": 0, "unit": "K^2h"}
@dataclass
class StudyParameters:
building: str
weather: str
temp_sup: float
ope_env: str
hr_type: str = None
hr_location: str = None
def get_name(self):
if self.hr_type is None and self.hr_location is None:
return "_".join([self.weather, self.building, self.ope_env, str(self.temp_sup)])
return "_".join([self.weather, self.building, self.hr_location, self.ope_env, str(self.temp_sup), self.hr_type])
def get_short_name(self):
if self.hr_type is None and self.hr_location is None:
return "_".join([
self.weather[0].upper(),
self.building.split("_")[1] + self.building.split("_")[2][0].upper(),
self.ope_env, str(self.temp_sup)
])
return self.get_name()
def get_multi_key(self):
return (
self.weather,
self.building,
self.hr_location,
self.ope_env,
self.temp_sup,
self.hr_type
)
def is_valid_case(self):
if self.building.endswith("_retrofit") and self.temp_sup > 60:
return False
return True
def xs_df(self, df):
return df.xs(self.weather, level=0).xs(self.building, level=0).xs(self.ope_env, level=1).xs(str(self.temp_sup), level=1)
class StudyRunner:
loc_rename = {
"AfterHeatPump": "After Heat Pump",
"Storage": "Storage",
"AfterStorage": "After Storage"
}
ctrl_rename = {
"OnOff": "On/Off",
"Modulating": "Modulating",
"Discrete": "Stepwise"
}
TSup_nominal = [
55,
75
]
operational_envelopes = {
"high": "[-20,60; -10,70; 40,70]",
"low": "[-20, 50;-10, 60;30, 60;35,55]"
}
heating_rod_locations = [
"AfterHeatPump",
"Storage",
"AfterStorage"
]
heating_rod_types = {
"OnOff": 1,
"Modulating": 0,
"Discrete": 5
}
QHRDHW_flow_nominal = get_dhw_size(profile="M", heat_loss_per_day=1.5, temperature_set=50)
comfort_metric = "dTCom"
comfort_boundary = 3600 * 73
outputs = {
"outputs.hydraulic.generation.WHPel.integral": {**_KWH_PLOTS, "label": "$W_\mathrm{el,HP}$", "rename": "WHPel", "plot": True},
"outputs.hydraulic.generation.WHRel.integral": {**_KWH_PLOTS, "label": "$W_\mathrm{el,HR}$", "plot": True},
"outputs.building.dTComHea[1]": {**_KH_PLOTS, "label": "$\Delta T_\mathrm{Comfort}$", "rename": "dTCom", "plot": True},
"outputs.hydraulic.generation.QHP_flow.integral": {**_KWH_PLOTS, "label": "$\dot{Q}_\mathrm{HP}$", "rename": "QHP", "plot": True},
"outputs.DHW.Q_flow.integral": {**_KWH_PLOTS, "label": "$\dot{Q}_\mathrm{DHW}$", "rename": "QDHW", "plot": True},
"outputs.building.QTraGain[1].integral": {**_KWH_PLOTS, "label": "$\dot{Q}_\mathrm{Tra}$", "rename": "QBui", "plot": True},
"outputs.hydraulic.storage.WelHRDHW.integral": {**_KWH_PLOTS, "label": "$W_\mathrm{el,HR,DHW}$", "plot": True},
"outputs.hydraulic.storage.WelHRAftBuf.integral": {**_KWH_PLOTS, "label": "$W_\mathrm{el,HR,AftSto}$", "plot": True},
"outputs.hydraulic.storage.WelHRBufSto.integral": {**_KWH_PLOTS, "label": "$W_\mathrm{el,HR,Sto}$", "plot": True},
"outputs.hydraulic.storage.QBufLoss.integral": {**_KWH_PLOTS, "label": "$\dot{Q}_\mathrm{Loss,Buf}$", "rename": "QBufLoss", "plot": True},
"outputs.hydraulic.storage.QDHWLoss.integral": {**_KWH_PLOTS, "label": "$\dot{Q}_\mathrm{Loss,DHW}$", "rename": "QDHWLoss", "plot": True},
"outputs.hydraulic.generation.WHPel.numSwi": {**_N_PLOTS, "rename": "numSwiHP", "plot": True, "label": "$N_\mathrm{Swi,HP}$"},
"outputs.hydraulic.generation.WHRel.numSwi": {**_N_PLOTS, "rename": "numSwiHR", "plot": True, "label": "$N_\mathrm{Swi,AftHR}$"},
"outputs.hydraulic.storage.WelHRDHW.numSwi": {**_N_PLOTS, "rename": "numSwiHRDHW", "plot": True, "label": "$N_\mathrm{Swi,DHW}$"},
"outputs.hydraulic.storage.WelHRAftBuf.numSwi": {**_N_PLOTS, "rename": "numSwiHRAftBuf", "plot": True, "label": "$N_\mathrm{Swi,HR,Buf}$"},
"outputs.hydraulic.storage.WelHRBufSto.numSwi": {**_N_PLOTS, "rename": "numSwiHRBuf", "plot": True, "label": "$N_\mathrm{Swi,HR,AftBuf}$"},
"hydraulic.control.securityControl.operationalEnvelope.ERR": {**_N_PLOTS, "rename": "opeEnvERR", "plot": True, "label": "$N_\mathrm{OE,HP}$"},
"hydraulic.control.HRPIDControlBuf.IAE": {**_KH_PLOTS, "rename": "IAE_Buf", "label": "$IAE_\mathrm{Buf}$", "plot": True},
"hydraulic.control.HRPIDControlDHW.IAE": {**_KH_PLOTS, "rename": "IAE_DHW", "label": "$IAE_\mathrm{DHW}$", "plot": True},
"hydraulic.control.HRPIDControlBuf.ISE": {**_KH2_PLOTS, "rename": "ISE_Buf", "label": "$ISE_\mathrm{Bui}$", "plot": True},
"hydraulic.control.HRPIDControlDHW.ISE": {**_KH2_PLOTS, "rename": "ISE_DHW", "label": "$ISE_\mathrm{DHW}$", "plot": True},
"outputs.hydraulic.control.IAE_Bui": {**_KH_PLOTS, "rename": "IAE_Bui", "label": "$IAE_\mathrm{Bui}$", "plot": True},
"outputs.hydraulic.control.ISE_Bui": {**_KH2_PLOTS, "rename": "ISE_Bui", "label": "$ISE_\mathrm{Bui}$", "plot": True},
"outputs.hydraulic.generation.WPumpel.integral": {**_KWH_PLOTS, "rename": "", "plot": True},
"outputs.hydraulic.generation.WPumpel.totalOnTime": {"factor": 1/3600, "offset": 0, "unit": "h", "rename": "timePump", "plot": True},
}
calculated_outputs = {
"Wel": {**_KWH_PLOTS, "label": "$FEC$", "plot": True},
"SCOP_HP": {**_N_PLOTS, "label": "$SCOP_\mathrm{HP}$", "plot": True},
"SCOP": {**_N_PLOTS, "label": "$SCOP$", "plot": True},
"Savings": {"factor": 100, "offset": 0, "label": "Savings", "unit": "%", "plot": True},
"partHRQ": {"factor": 100, "offset": 0, "label": r"$\alpha_\mathrm{BH}$", "unit": "%", "plot": True},
"KRel_4.8": {"factor": 1, "offset": 0, "label": r"$R_\mathrm{EGR=4.8}$", "unit": "-", "plot": True},
"KRel_1": {"factor": 1, "offset": 0, "label": r"$R_\mathrm{EGR=1}$", "unit": "-", "plot": True},
"KRel_2.1": {"factor": 1, "offset": 0, "label": r"$R_\mathrm{EGR=2.1}$", "unit": "-", "plot": True},
}
heating_rod_integrals = [
"outputs.hydraulic.generation.WHRel.integral",
"outputs.hydraulic.storage.WelHRDHW.integral",
"outputs.hydraulic.storage.WelHRAftBuf.integral",
"outputs.hydraulic.storage.WelHRBufSto.integral"]
def __init__(self, study_name, use_ope_env_ctrl=True):
self.basepath = pathlib.Path(r"D:\02_Paper\01_Energies")
self.target_mos_file = self.basepath.joinpath("heating_rod_integration_study", "OptimalHeatingRodIntegration", "Resources", "WeatherData.mos")
self.bes_path = pathlib.Path(r"D:\04_git\BESModPaper\BESMod")
self.dym_api = None
self.use_ope_env_ctrl = use_ope_env_ctrl
self.cwd = self.basepath.joinpath("Results", study_name)
os.makedirs(self.cwd, exist_ok=True)
def load_excel_setup(self):
weather_buildings = pd.read_excel(
self.basepath.joinpath("heating_rod_integration_study", "Scenarios.xlsx"),
sheet_name="BuildingsAndWeather"
)
weather_buildings = weather_buildings.set_index("type")
wea = ["name", "path", "color", "TOdaNom"]
weather = weather_buildings.loc[:, wea]
buildings = weather_buildings.drop(wea, axis=1)
return weather, buildings
def _get_study_product(self):
weather, buildings = self.load_excel_setup()
return [
buildings.columns,
self.heating_rod_locations,
self.operational_envelopes.keys(),
self.TSup_nominal,
list(self.heating_rod_types.keys())
]
def get_cases_for_weather(self, weather, only_valid):
cases = []
product = self._get_study_product()
for bui_typ, hr_loc, ope_env, t_sup, hr_typ in itertools.product(*product):
case = StudyParameters(
building=bui_typ,
hr_type=hr_typ,
hr_location=hr_loc,
ope_env=ope_env,
temp_sup=t_sup,
weather=weather
)
if case.is_valid_case() or (not only_valid):
if "1" in case.building:
cases.append(case)
return cases
def run_study(self, recalculate=False):
self.dym_api = DymolaAPI(
cd=self.basepath.joinpath("00_DymolaWorkDir"),
model_name="OptimalHeatingRodIntegration.Cases.Case1",
packages=[
self.bes_path.joinpath("BESMod", "package.mo"),
self.bes_path.joinpath("installed_dependencies", "IBPSA", "IBPSA", "package.mo"),
self.bes_path.joinpath("installed_dependencies", "AixLib", "AixLib", "package.mo"),
self.bes_path.joinpath("installed_dependencies", "Buildings", "Buildings", "package.mo"),
self.bes_path.joinpath("installed_dependencies", "BuildingSystems", "BuildingSystems", "package.mo"),
self.basepath.joinpath("heating_rod_integration_study", "OptimalHeatingRodIntegration", "package.mo")
],
n_cpu=12,
show_window=True
)
self.dym_api.sim_setup.stop_time = 365 * 86400
self.dym_api.sim_setup.output_interval = 15 * 60
self.dym_api.result_names = list(self.dym_api.outputs.keys())
model_name = self.dym_api.model_name
if model_name != self.dym_api.model_name:
self.dym_api.model_name = model_name
weather, buildings = self.load_excel_setup()
for wea_typ in weather.index:
# Copy file:
shutil.copyfile(
self.basepath.joinpath(
"heating_rod_integration_study",
"OptimalHeatingRodIntegration",
"Resources",
weather.loc[wea_typ, "path"]),
self.target_mos_file)
model_names = []
result_names = []
cases = self.get_cases_for_weather(weather=wea_typ, only_valid=True)
for case in cases:
if not recalculate:
if os.path.isfile(self.cwd.joinpath(case.get_name()+".mat")):
print("Case %s already simulated. Skipping ..." % case.get_name())
continue
hr_typ_data = self.heating_rod_types[case.hr_type]
result_names.append(case.get_name())
QBui_flow_nominal = buildings.loc[case.weather, case.building]
model_names.append(
"OptimalHeatingRodIntegration.Cases.PythonAPIModel("
f"\n\t\tzoneParam=OptimalHeatingRodIntegration.Buildings.{case.building}(), "
f"\n\t\tdiscretizationStepsHR={hr_typ_data}, "
f"\n\t\thr_location=OptimalHeatingRodIntegration.Models.HRLocation.{case.hr_location}, "
f"\n\t\ttableUpp={self.operational_envelopes[case.ope_env]}, "
f"\n\t\tparameterStudy("
f"\n\t\t\tQHRDHW_flow_nominal={self.QHRDHW_flow_nominal}, " # TODO
f"\n\t\t\tQHRBuf_flow_nominal={QBui_flow_nominal}), " # TODO
f"\n\t\tsystemParameters("
f"\n\t\t\tQBui_flow_nominal=fill({QBui_flow_nominal}, 1), "
f"\n\t\t\tTOda_nominal={round(weather.loc[case.weather, 'TOdaNom'] + 273.15, 2)},"
f"\n\t\t\tTHydSup_nominal=fill({round(case.temp_sup + 273.15, 2)}, 1)),"
f"\n\t\t\tuse_opeEncControl={'true' if self.use_ope_env_ctrl else 'false'}"
"\n\t\t)"
)
self.dym_api.logger.info(f"Simulating weather file {wea_typ} with {len(model_names)} combinations")
self.dym_api.simulate(
model_names=model_names, # TODO: Make possible in ebcpy!
result_file_name=result_names,
savepath=str(self.cwd),
return_option="savepath",
fail_on_error=False
)
self.dym_api.logger.info(f"Finished simulation of {len(model_names)} combinations")
self.dym_api.close()
def extract_results(self):
weather, buildings = self.load_excel_setup()
product = self._get_study_product()
cols = pd.MultiIndex.from_product([weather.index.tolist()] + product,
names=["Weather", "Building", "HeatingRodLocation",
"OperationalEnvelope", "SupplyTemperature", "HeatingRodType"]
)
data = pd.DataFrame(columns=cols)
for wea_typ in weather.index:
cases = self.get_cases_for_weather(weather=wea_typ, only_valid=True)
for idx, case in enumerate(cases):
import time
file = self.cwd.joinpath(case.get_name() + ".mat")
if not os.path.exists(file):
data.loc[:, case.get_multi_key()] = np.NAN
else:
t0 = time.time()
values = TimeSeriesData(file).to_df().iloc[-1]
sec_loa = time.time() - t0
for out in self.outputs.keys():
if out in values:
data.loc[out, case.get_multi_key()] = values[out]
else:
print(out)
data.loc[out, case.get_multi_key()] = np.NAN
sec_ext = time.time() - t0 - sec_loa
logger.info("Extracted %s of %s files for weather %s. "
"Took %s s to load and %s s to extract.",
idx + 1, len(cases), wea_typ,
round(sec_loa, 2), round(sec_ext, 2))
data.to_csv(self.cwd.joinpath("Overall_result.csv"))
logger.info("Extracted data for weather %s", wea_typ)
return self.cwd.joinpath("Overall_result.csv")
def load_and_calc_data(self, path_csv):
df = pd.read_csv(path_csv, header=[0, 1, 2, 3, 4, 5], index_col=[0])
df = df.fillna(0)
rename = {k: v["rename"] for k, v in self.outputs.items() if "rename" in v}
df = df.rename(index=rename)
eta_HR = 0.97
df.loc["WHRel"] = df.loc[self.heating_rod_integrals].sum()
df.loc["QDem"] = df.loc["QBui"] + df.loc["QDHW"]
df.loc["Wel"] = df.loc["WHPel"] + df.loc["WHRel"]
df.loc["SCOP"] = df.loc["QDem"] / df.loc["Wel"]
df.loc["SCOP_HP"] = df.loc["QHP"] / df.loc["WHPel"]
df.loc["partHRel"] = df.loc["WHRel"] / df.loc["Wel"]
df.loc["partHRQ"] = df.loc["WHRel"] / df.loc["QDem"] * eta_HR
for k_EGR in [4.8, 2.1, 1]:
df.loc[f"KRel_{k_EGR}"] = 1 - df.loc["SCOP_HP"] ** -1 - df.loc["partHRQ"]*((eta_HR * k_EGR) ** -1 - df.loc["SCOP_HP"] ** -1)
return df
def get_all_boundary_cases(self):
weather, buildings = self.load_excel_setup()
all_cases = []
for wea in weather.index:
all_cases.extend(self.get_cases_for_weather(weather=wea, only_valid=False))
_cases = pd.DataFrame([c.__dict__ for c in all_cases])
all_cases = []
for wea in _cases["weather"].unique():
for bui in _cases["building"].unique():
if "2" in bui:
continue
for tsup in _cases["temp_sup"].unique():
for ope_env in _cases["ope_env"].unique():
all_cases.append(StudyParameters(
building=bui, weather=wea,
temp_sup=tsup, ope_env=ope_env
))
return all_cases
def get_plotting_options(self):
plot_data = {}
for k, v in {**self.outputs, **self.calculated_outputs}.items():
plot_data[v.get("rename", k)] = v.copy()
return plot_data
def plot(self, path_csv):
plot_values = self.get_plotting_options()
cases = self.get_all_boundary_cases()
df = self.load_and_calc_data(path_csv=path_csv)
df = df.transpose()
for case in cases:
if not case.is_valid_case():
continue
_d = case.xs_df(df).copy()
_d = _d.transpose()
_d.loc["Savings"] = (_d.loc["Wel"].max() - _d.loc["Wel"]) / _d.loc["Wel"].max()
_d = _d.transpose()
_d = _d.reset_index(level=[0, 1])
_d.index = _d.loc[:, "HeatingRodLocation"] + "_" + _d.loc[:, "HeatingRodType"]
_d = _d.sort_index()
# Print maximal savings where comfort is ok:
comfort_ok = _d.loc[_d[self.comfort_metric] < self.comfort_boundary]
if comfort_ok.empty:
print(case.get_name(), "None", 0, 0)
else:
idx = comfort_ok.loc[:, "Savings"].argmax()
idx_name = comfort_ok.index[idx]
print(case.get_name(), idx_name, comfort_ok.loc[:, "Savings"].max() * 100, comfort_ok.loc[idx_name, "SCOP"])
for col in _d.columns:
if col not in plot_values.keys():
continue
col_data = plot_values[col]
fig, ax = plt.subplots(figsize=(20, 10))
_d_col = _d.loc[:, col].copy()
_d_col *= col_data["factor"]
_d_col += col_data["offset"]
max = col_data.get("max")
if max is not None:
ax.set_xlim([_d_col.min() * 0.9, max])
else:
try:
ax.set_xlim([_d_col.min() * 0.9, _d_col.max() * 1.1])
except ValueError:
print("ASJKDHASKHD")
_d_col.plot.barh(ax=ax)
ax.set_xlabel(f'{col_data.get("label", col)} in {col_data.get("unit", "-")}')
path = pathlib.Path(path_csv).parent.joinpath(case.get_name())
os.makedirs(path, exist_ok=True)
plt.savefig(path.joinpath(f"{col}.png"))
plt.close("all")
def plot_mean_temps(self):
means = {
"hydraulic.generation.portGen_out[1].h_outflow":
{"label": "$T_\mathrm{Gen,Out}$", "color": "red", "linestyle": "-", "factor": 1 / 4184, "offset": 0},
"hydraulic.control.sigBusGen.hp_bus.TConInMea":
{"label": "$T_\mathrm{HP,In}$", "color": "blue", "linestyle": "-"},
"hydraulic.distribution.senTBuiSup.T":
{"label": "$T_\mathrm{Buf,Out}$", "color": "red", "linestyle": "--"},
"hydraulic.control.sigBusDistr.TStoBufTopMea":
{"label": "$T_\mathrm{Buf,Top}$", "color": "red", "linestyle": "--"},
"hydraulic.control.sigBusDistr.TStoBufBotMea":
{"label": "$T_\mathrm{HP,In}$", "color": "blue", "linestyle": "-"},
"hydraulic.control.sigBusDistr.TStoDHWTopMea":
{"label": "$T_\mathrm{DHW,Top}$", "color": "red", "linestyle": "-."},
"hydraulic.control.sigBusDistr.TStoDHWBotMea":
{"label": "$T_\mathrm{DHW,Bot}$", "color": "blue", "linestyle": "-."},
"hydraulic.transfer.rad[1].vol[5].T":
{"label": "$T_\mathrm{Rad,Out}$", "color": "blue", "linestyle": "--"},
"hydraulic.transfer.rad[1].vol[1].T":
{"label": "$T_\mathrm{Rad,In}$", "color": "red", "linestyle": "--"}
}
results = {}
names = {}
cases = self.get_all_boundary_cases()
all_cases = []
for case in cases:
for hr_type in self.heating_rod_types:
for hr_loc in self.heating_rod_locations:
all_cases.append(StudyParameters(**{k:v for k, v in case.__dict__.items() if v is not None}, hr_location=hr_loc, hr_type=hr_type))
for case in all_cases:
if not case.is_valid_case():
continue
df = TimeSeriesData(self.cwd.joinpath(case.get_name() + ".mat")).to_df()
temp_res = {}
for mean_name, options in means.items():
temp_res[mean_name] = df.loc[:, mean_name].mean() * options.get("factor", 1) + options.get("offset", -273.15)
names[case.get_name()] = case.__dict__
results[case.get_name()] = temp_res
df = pd.DataFrame(results)
df = df.transpose()
df_n = pd.DataFrame(names)
df_n = df_n.transpose()
df = pd.concat([df, df_n], axis=1)
df.to_excel(self.cwd.joinpath("Mean_temperatures.xlsx"))
return results
def plot_time_series(self, cases: list, start: float, end: float, scenario: dict, plot_temperatures: dict, temp_names: str):
import matplotlib
matplotlib.use("pgf")
matplotlib.rcParams.update({
"pgf.texsystem": "pdflatex",
'font.family': 'serif',
'text.usetex': True,
'font.size': 14,
'pgf.rcfonts': False,
'hatch.linewidth': 5
})
_cases_full = [StudyParameters(**case, **scenario) for case in cases]
fig, ax = plt.subplots(len(_cases_full), 2, sharex=True, figsize=(13, 12))
max_w = 0
max_n = 0
twinaxes = []
for idx, case in enumerate(_cases_full):
df = TimeSeriesData(self.cwd.joinpath(case.get_name() + ".mat")).to_df()
df.index /= 86400
df = df.loc[start:end]
WelHP = df.loc[:, "outputs.hydraulic.generation.WHPel.integral"].copy()
WelHR = 0
for hr in self.heating_rod_integrals:
if hr in df.columns:
WelHR += df.loc[:, hr]
WelHR /= 3600000
WelHR -= WelHR.iloc[0]
WelHP /= 3600000
WelHP -= WelHP.iloc[0]
Wel = WelHR + WelHP
if max_w < Wel.max():
max_w = Wel.max()
_p = deepcopy(plot_temperatures)
for val, plot_settings in _p.items():
offset = plot_settings.pop("offset", 273.15)
factor = plot_settings.pop("factor", 1)
ax[idx, 0].plot(df.loc[:, val] * factor - offset, **plot_settings)
ax[idx, 1].plot(Wel, color="black", linestyle="-", label="$FEC$ in kWh")
ax[idx, 1].plot(WelHP, color="green", linestyle="-", label="$FEC_\mathrm{HP}$ in kWh")
ax[idx, 1].plot(WelHR, color="green", linestyle="--", label="$FEC_\mathrm{BH}$ in kWh")
ax[idx, 1].set_ylabel("$FEC$ in kWh")
tax = ax[idx, 1].twinx()
tax.set_ylabel("On/Off-Switches")
nHP = df.loc[:, "outputs.hydraulic.generation.WHPel.numSwi"]
if (nHP - nHP.iloc[0]).max() > max_n:
max_n = (nHP - nHP.iloc[0]).max()
#tax.plot(nHP - nHP.iloc[0], label="$N_\mathrm{Swi,HP}$", color="gray", linestyle="-")
nERR = df.loc[:, "hydraulic.control.securityControl.operationalEnvelope.ERR"]
if (nERR - nERR.iloc[0]).max() > max_n:
max_n = (nERR - nERR.iloc[0]).max()
tax.plot(nERR - nERR.iloc[0], label="$N_\mathrm{ERR}$", color="gray", linestyle="--")
twinaxes.append(tax)
ax[idx, 0].set_title(self.loc_rename["_".join(list(cases[idx].values()))])
ax[idx, 1].set_title(self.loc_rename["_".join(list(cases[idx].values()))])
ax[idx, 0].set_ylabel("$T$ in °C")
for _ax in ax[:, 1]:
_ax.set_ylim([0, max_w])
for tax in twinaxes:
tax.set_ylim([0, max_n])
th, tl = twinaxes[0].get_legend_handles_labels()
h, l = ax[0, 1].get_legend_handles_labels()
ax[0, 1].legend(h + th, l + tl, loc="lower left", bbox_to_anchor=(0, 1.15), ncol=2)
ax[0, 0].legend(loc="lower left", bbox_to_anchor=(0, 1.15), ncol=3)
ax[-1, 0].set_xlabel("Time in d")
ax[-1, 1].set_xlabel("Time in d")
fig.align_ylabels()
plt.savefig(self.cwd.joinpath("_".join([str(v) for v in scenario.values()]) + f"_{temp_names}.pgf"))
print(self.cwd.joinpath("_".join([str(v) for v in scenario.values()]) + f"_{temp_names}.pgf"))
plt.close("all")
def plot_boxplot(self,
path_csv, metric, relative,
with_markers=True,
with_box=True,
sort=None,
cases=None,
ax: plt.Axes = None,
set_ylabels: bool = True,
set_legend: bool = True
):
plot_data = self.get_plotting_options()[metric]
df = self.load_and_calc_data(path_csv=path_csv)
df = df.transpose()
df = df.loc[df.loc[:, self.comfort_metric] <= self.comfort_boundary, metric]
df *= plot_data["factor"]
df += plot_data["offset"]
if ax is None:
fig, ax = plt.subplots(1, 1)
_return_later = False
else:
_return_later = True
labels = []
data = []
if cases is None:
cases = self.get_all_boundary_cases()
for case in cases:
if not case.is_valid_case():
continue
try:
_df = case.xs_df(df)
if relative:
data.append((_df - _df.median()) / _df.median() * 100)
print(case.get_name(), data[-1].max(), data[-1].min())
else:
if metric == "Wel":
print(case.get_name(), "_".join(_df.index[_df.argmax()]))
data.append(_df - _df.median())
#elif metric in ["numSwiHP", "opeEnvERR"]:
# data.append(_df - _df.median())
else:
data.append(_df)
labels.append(case.get_short_name())
except KeyError:
print(f"Could not find case {case.get_name()} in data")
labels_sorted = []
data_sorted = []
if sort:
if sort == "min":
min_maxs = [d.min() for d in data]
elif sort == "max":
min_maxs = [d.max() for d in data]
else:
raise KeyError
for _, _label, _data in sorted(zip(min_maxs, labels, data)):
labels_sorted.append(_label)
data_sorted.append(_data)
data = data_sorted
labels = labels_sorted
if with_box:
ax.boxplot(data, whis=1e6, vert=0)
if with_markers:
for idx, d in enumerate(data):
_d, legend_elements = self._get_markers_for_combis(d.copy().reset_index())
for _, row in _d.iterrows():
ax.scatter(row[metric], [idx+1], color=row["color"], marker=row["marker"], s=100)
if set_legend:
ax.legend(handles=legend_elements, loc="lower left", bbox_to_anchor=(0, 1), ncol=6)
if not with_box and not with_markers:
raise ValueError("Not a valid option. Nothing will be plotted...")
if relative:
y_label = f"Deviation of {plot_data['label']} in %"
else:
y_label = plot_data["label"] + " in " + plot_data["unit"]
ax.set_xlabel(y_label)
if not _return_later:
ax.set_yticklabels(labels, ha="right")
plt.savefig(path_csv.parent.joinpath(f"boxplots_{metric}_relative_{relative}.png"))
else:
return labels
def _get_markers_for_combis(self, df):
marker_hr_locaction = {
"AfterHeatPump": "o",
"Storage": "^",
"AfterStorage": "s"
}
color_hr_type = {
"OnOff": "green",
"Discrete": "blue",
"Modulating": "red"
}
legend_elements = []
df["marker"] = None
df["color"] = None
from matplotlib.patches import Patch
from matplotlib.lines import Line2D
for loc, marker in marker_hr_locaction.items():
df.loc[df["HeatingRodLocation"] == loc, "marker"] = marker
legend_elements.append(Line2D([0], [0], marker=marker, color='black', label=self.loc_rename[loc],
markerfacecolor='black', markersize=10))
for typ, color in color_hr_type.items():
df.loc[df["HeatingRodType"] == typ, "color"] = color
legend_elements.append(Patch(facecolor=color, edgecolor=color, label=self.ctrl_rename[typ]))
return df, legend_elements
def plot_comfort_vs_costs(self, path_csv, metric_costs="Wel", metric_comfort="dTCom"):
import matplotlib
#matplotlib.use("pgf")
#matplotlib.rcParams.update({
# "pgf.texsystem": "pdflatex",
# 'font.family': 'serif',
# 'text.usetex': True,
# 'font.size': 14,
# 'pgf.rcfonts': False,
# 'hatch.linewidth': 5
#})
plot_data = self.get_plotting_options()
plot_costs = plot_data[metric_costs]
plot_comfort = plot_data[metric_comfort]
cases = self.get_all_boundary_cases()
df = self.load_and_calc_data(path_csv=path_csv)
df = df.transpose()
df.loc[:, metric_costs] *= plot_costs["factor"]
df.loc[:, metric_costs] += plot_costs["offset"]
df.loc[:, metric_comfort] *= plot_comfort["factor"]
df.loc[:, metric_comfort] += plot_comfort["offset"]
savings_comfort_step_vs_mod = []
savings_costs_step_vs_mod = []
for case in cases:
if not case.is_valid_case():
continue
fig, ax = plt.subplots(1, 1, figsize=(5, 5))
_df = case.xs_df(df).copy()
_df = _df.reset_index()
_df, legend_elements = self._get_markers_for_combis(_df)
if np.all(_df.loc[:, "dTCom"] < 73):
_dis = _df.loc[_df["HeatingRodType"] == "Discrete", metric_costs]
_mod = _df.loc[_df["HeatingRodType"] == "Modulating", metric_costs]
_sav = (_dis.values - _mod.values) / _mod.values * 100
savings_costs_step_vs_mod.extend(list(_sav))
if np.any(_sav < -500):
print(case.get_name())
_dis = _df.loc[_df["HeatingRodType"] == "Discrete", metric_comfort]
_mod = _df.loc[_df["HeatingRodType"] == "Modulating", metric_comfort]
_sav = (_mod.values - _dis.values)
savings_comfort_step_vs_mod.extend(list(_sav))
for _, row in _df.iterrows():
ax.scatter(row[metric_costs], row[metric_comfort], color=row["color"], marker=row["marker"], s=100)
ax.legend(handles=legend_elements, loc='upper right')
ax.set_xlabel(plot_costs["label"] + " in " + plot_costs["unit"])
ax.set_ylabel(plot_comfort["label"] + " in " + plot_comfort["unit"])
fig.tight_layout()
plt.savefig(path_csv.parent.joinpath(f"comfort_vs_costs_{case.get_name()}.png"))
plt.close("all")
_rel = np.array(savings_comfort_step_vs_mod) / np.array(savings_costs_step_vs_mod)
print(_rel.mean(), _rel.max(), _rel.min())
plt.scatter(savings_costs_step_vs_mod, savings_comfort_step_vs_mod, color="blue", s=100)
plt.xlabel("Deviation of $FEC$ in %")
plt.ylabel("Comfort difference in Kh")
plt.show()
return savings_comfort_step_vs_mod, savings_costs_step_vs_mod
def plot_temps(self):
plot_temperatures_1 = {
"hydraulic.generation.portGen_out[1].h_outflow":
{"label": "$T_\mathrm{Gen,Out}$", "color": "red", "linestyle": "-", "factor": 1 / 4184, "offset": 0},
#"hydraulic.control.sigBusGen.hp_bus.TConInMea":
# {"label": "$T_\mathrm{HP,In}$", "color": "blue", "linestyle": "-"},
"hydraulic.distribution.senTBuiSup.T":
{"label": "$T_\mathrm{Buf,Out}$", "color": "red", "linestyle": "--"},
#"hydraulic.control.sigBusDistr.TStoBufTopMea":
# {"label": "$T_\mathrm{Buf,Top}$", "color": "red", "linestyle": "--"},
"hydraulic.control.sigBusDistr.TStoBufBotMea":
{"label": "$T_\mathrm{HP,In}$", "color": "blue", "linestyle": "-"},
# "hydraulic.control.sigBusDistr.TStoDHWTopMea":
# {"label": "$T_\mathrm{DHW,Top}$", "color": "red", "linestyle": "-."},
# "hydraulic.control.sigBusDistr.TStoDHWBotMea":
# {"label": "$T_\mathrm{DHW,Bot}$", "color": "blue", "linestyle": "-."},
"hydraulic.control.operationalEnvelopeLimit.uppCombiTable1Ds.y[1]":
{"label": "$T_\mathrm{Max}$", "color": "black", "linestyle": "--", "offset": 0},
"hydraulic.control.operationalEnvelopeLimit.TSet":
{"label": "$T_\mathrm{Set}$", "color": "black", "linestyle": "-."},
}
plot_temperatures_2 = {
"hydraulic.control.sigBusGen.hp_bus.TConInMea":
{"label": "$T_\mathrm{Gen,In}$", "color": "blue", "linestyle": "-"},
"hydraulic.distribution.storageBuf.layer[4].T":
{"label": "$T_\mathrm{Buf,Top}$", "color": "red", "linestyle": "-."},
"hydraulic.distribution.storageBuf.layer[1].T":
{"label": "$T_\mathrm{Buf,Bot}$", "color": "blue", "linestyle": "-."},
"hydraulic.transfer.rad[1].vol[5].T":
{"label": "$T_\mathrm{Rad,Out}$", "color": "blue", "linestyle": "--"},
"hydraulic.transfer.rad[1].vol[1].T":
{"label": "$T_\mathrm{Rad,In}$", "color": "red", "linestyle": "--"},
"outputs.building.TZone[1]":
{"label": "$T_\mathrm{Room}$", "color": "black", "linestyle": "-"},
}
#for hr_type in self.heating_rod_types:
hr_type = "Modulating"
scenario = dict(building="Case_1_standard", weather="warm", temp_sup=75, ope_env="low", hr_type=hr_type)
cases = [{"hr_location": loc} for loc in self.heating_rod_locations]
self.plot_time_series(start=14.5, end=15.5, scenario=scenario, cases=cases,
plot_temperatures=plot_temperatures_1, temp_names="Sto")
#self.plot_time_series(start=15, end=16, scenario=scenario, cases=cases,
# plot_temperatures=plot_temperatures_2, temp_names="Retu")
def _plot_multiple_boxplots(self, path_csv, metrics, cases, name, relative=None, xticks=None, xlim=None):
_n = len(metrics)
if relative is None:
relative = [False] * _n
if cases is None:
size = 8
elif len(cases) == 10:
size = 8
else:
size = round(8 * 6 / 10, 0) + 0.5
fig, ax = plt.subplots(1, _n, sharey=True, figsize=(15, size))
fig.subplots_adjust(hspace=0, wspace=0)
for idx, metric in enumerate(metrics):
if idx == 0:
labels = self.plot_boxplot(
path_csv=path_csv, relative=relative[idx], metric=metric,
cases=cases, ax=ax[idx], set_ylabels=True, set_legend=True
)
else:
self.plot_boxplot(
path_csv=path_csv, relative=relative[idx], metric=metric,
cases=cases, ax=ax[idx], set_ylabels=False, set_legend=False
)
ax[0].set_yticklabels(labels * _n, ha="right")
if xticks is not None:
if isinstance(xticks[0], list):
for _ax, ticks, lim in zip(ax, xticks, xlim):
_ax.set_xticks(ticks)
_ax.set_xlim(lim)
else:
for _ax in ax:
_ax.set_xticks(xticks)
_ax.set_xlim(xlim)
plt.savefig(path_csv.parent.joinpath(f"boxplots_results_{name}.png"))
if cases is not None:
self._plot_multiple_boxplots(
path_csv=path_csv, metrics=metrics,
cases=None, name=f"{name}_appendix", relative=relative, xlim=xlim, xticks=xticks)
def plot_result_boxplots(self, path_csv):
cases_savings = [
StudyParameters(weather="warm", temp_sup=75, ope_env="low", building="Case_1_standard"),
StudyParameters(weather="average", temp_sup=75, ope_env="low", building="Case_1_standard"),
StudyParameters(weather="cold", temp_sup=75, ope_env="low", building="Case_1_standard"),
StudyParameters(weather="cold", temp_sup=75, ope_env="high", building="Case_1_standard"),
StudyParameters(weather="warm", temp_sup=55, ope_env="low", building="Case_1_standard"),
StudyParameters(weather="warm", temp_sup=55, ope_env="low", building="Case_1_retrofit"),
StudyParameters(weather="average", temp_sup=55, ope_env="low", building="Case_1_retrofit"),
StudyParameters(weather="cold", temp_sup=55, ope_env="low", building="Case_1_standard"),
StudyParameters(weather="cold", temp_sup=55, ope_env="low", building="Case_1_retrofit"),
][::-1]
self._plot_multiple_boxplots(
path_csv=path_csv, cases=cases_savings,
metrics=["Wel", "SCOP_HP", "opeEnvERR", "partHRQ"],
name="savings",
relative=[True, False, False, False],
xticks=[[-15, 0, 15], [2.5, 3, 3.5], [0, 1000, 2000], [0, 20, 40]],
xlim=[[-20, 20], [2, 4], [0, 2500], [0, 40]]
)
cases_comfort = [
StudyParameters(weather="warm", temp_sup=75, ope_env="low", building="Case_1_standard"),
StudyParameters(weather="average", temp_sup=75, ope_env="low", building="Case_1_standard"),
StudyParameters(weather="cold", temp_sup=75, ope_env="low", building="Case_1_standard"),
StudyParameters(weather="cold", temp_sup=75, ope_env="high", building="Case_1_standard"),
StudyParameters(weather="cold", temp_sup=55, ope_env="high", building="Case_1_standard"),
StudyParameters(weather="cold", temp_sup=55, ope_env="low", building="Case_1_retrofit"),
][::-1]
# round(8 * 6 / 10, 0) + 0.5))
prio_bound = self.comfort_boundary
self.comfort_boundary = 3600 * 1000
self._plot_multiple_boxplots(
path_csv=path_csv, cases=cases_comfort,
metrics=["Wel", "dTCom"],
name="comfort"
)
self.comfort_boundary = prio_bound
#self._plot_multiple_boxplots(
# path_csv=path_csv, cases=None,
# metrics=["SCOP", "SCOP_HP"],
# name="SCOP"
#)
self._plot_multiple_boxplots(
path_csv=path_csv, cases=cases_savings,
metrics=["KRel_4.8", "KRel_2.1", "KRel_1"],
name="EGR",
xticks=[0.4, 0.5, 0.6, 0.7],
xlim=[0.35, 0.75]
)
if __name__ == "__main__":
STUDY_NAME = "StudyOneYear"
RUNNER = StudyRunner(study_name=STUDY_NAME, use_ope_env_ctrl=True)
RUNNER.run_study()
PATH_CSV = RUNNER.extract_results()
RUNNER.plot(path_csv=PATH_CSV)
RUNNER.plot_result_boxplots(path_csv=PATH_CSV)
RUNNER.plot_comfort_vs_costs(path_csv=PATH_CSV)
RUNNER.plot_temps()