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hplib_database.py
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hplib_database.py
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import os
import pandas as pd
import numpy as np
import scipy
import hplib as hpl
import pickle
import requests
from bs4 import BeautifulSoup
from datetime import datetime
import shutil
# get path to the current file
THIS_FOLDER_PATH = os.path.dirname(os.path.abspath(__file__))
# Functions
def import_keymark_data(i=0):
# create folder to save csv files
foldername = "csv/"
# open main page of EHPA
manufacturers_info = BeautifulSoup(
requests.get("https://www.heatpumpkeymark.com/?type=109126").content,
"html.parser",
)
# look for all manufacturers
for manufacturer_info in manufacturers_info.find_all("td")[i:]:
i += 1
print(
"Progress: ",
i,
" / ",
len(manufacturers_info.find_all("td")),
" In case of error: restart function with ",
i - 1,
" as input of function",
)
manufacturer = manufacturer_info.text
models_info = BeautifulSoup(
requests.get(
"https://www.heatpumpkeymark.com/" + manufacturer_info.a.get("href")
).content,
"html.parser",
)
# look for models:
for model in models_info.find_all("td"):
if model.text != manufacturer:
filename = manufacturer + model.text.replace("/", "_")
filename = filename.replace(" ", "_")
if (
os.path.isfile(
THIS_FOLDER_PATH + "/../input/csv/" + filename + ".csv"
)
== 0
):
try:
model_info = BeautifulSoup(
requests.get(
"https://www.heatpumpkeymark.com/" + model.a.get("href")
).content,
"html.parser",
)
except:
continue
for info_col in model_info.find_all(class_="info-coll"):
if (info_col.find(class_="info-label").span.text) == (
"Refrigerant"
):
ref = (
info_col.find(class_="info-data")
.text.replace(" ", "")
.replace("\n", "")
.replace("\r", "")
)
if (info_col.find(class_="info-label").span.text) == (
"Mass of Refrigerant"
):
mass_of_ref = info_col.find(
class_="info-data"
).text.replace("\n", "")
if (info_col.find(class_="info-label").span.text) == (
"Certification Date"
):
date = (
info_col.find(class_="info-data")
.text.replace(" ", "")
.replace("\n", "")
.replace("\r", "")
)
if (info_col.find(class_="info-label").span.text) == (
"Heat Pump Type"
):
type = info_col.find(class_="info-data").text.replace(
"\n", ""
)[13:-9]
if (info_col.find(class_="info-label").span.text) == (
"Driving energy"
):
energy = info_col.find(class_="info-data").text.replace(
"\n", ""
)[13:-9]
# choose correct export method (csv preffered) and get basic data TODO
for export_method in model_info.find_all("a"):
if export_method.text.startswith("Export"):
soup3 = BeautifulSoup(
requests.get(
"https://www.heatpumpkeymark.com/"
+ export_method.get("href")
).content,
"html.parser",
)
# open Download link
for link in soup3.find_all("a"):
if link.text == "Download":
# write to csv
csv_content = requests.get(
"https://www.heatpumpkeymark.com/"
+ link.get("href")
).content
with open(
THIS_FOLDER_PATH
+ "/../input/"
+ foldername
+ filename
+ ".csv",
"wb",
) as file:
file.write(csv_content)
with open(
THIS_FOLDER_PATH
+ "/../input/"
+ foldername
+ filename
+ ".csv",
"a",
) as f:
f.write(
'"","Refrigerant","'
+ str(ref)
+ '","0","0","0","0"\r\n'
)
f.write(
'"","Mass of Refrigerant","'
+ str(mass_of_ref)
+ '","0","0","0","0"\r\n'
)
f.write(
'"","Date","'
+ str(date)
+ '","0","0","0","0"\r\n'
)
f.write(
'"","Manufacturer","'
+ manufacturer
+ '","0","0","0","0"\r\n'
)
f.write(
'"","Modelname","'
+ model.text
+ '","0","0","0","0"\r\n'
)
f.write(
'"","Type","'
+ type
+ '","0","0","0","0"\r\n'
)
f.write(
'"","Energy","'
+ energy
+ '","0","0","0","0"\r\n'
)
def combine_raw_csv(foldername):
df_all = pd.DataFrame()
(
manufacturers,
models,
titels,
dates,
types,
refrigerants,
mass_of_refrigerants,
supply_energy,
spl_indoors,
spl_outdoors,
eta,
p_rated,
scop,
t_biv,
tol,
p_th_minus7,
cop_minus7,
p_th_2,
cop_2,
p_th_7,
cop_7,
p_th_12,
cop_12,
p_th_tbiv,
cop_tbiv,
p_th_tol,
cop_tol,
rated_airflows,
wtols,
poffs,
ptos,
psbs,
pcks,
supp_energy_types,
p_sups,
p_design_cools,
seers,
pdcs_35,
eer_35,
pdcs_30,
eer_30,
pdcs_25,
eer_25,
pdcs_20,
eer_20,
temperatures,
) = ([] for i in range(46))
lists = [
manufacturers,
models,
titels,
dates,
types,
refrigerants,
mass_of_refrigerants,
supply_energy,
spl_indoors,
spl_outdoors,
eta,
p_rated,
scop,
t_biv,
tol,
p_th_minus7,
cop_minus7,
p_th_2,
cop_2,
p_th_7,
cop_7,
p_th_12,
cop_12,
p_th_tbiv,
cop_tbiv,
p_th_tol,
cop_tol,
rated_airflows,
wtols,
poffs,
ptos,
psbs,
pcks,
supp_energy_types,
p_sups,
p_design_cools,
seers,
pdcs_35,
eer_35,
pdcs_30,
eer_30,
pdcs_25,
eer_25,
pdcs_20,
eer_20,
temperatures,
]
values = [
"Manufacturer",
"Modelname",
"title",
"Date",
"application",
"Refrigerant",
"Mass of Refrigerant",
"Energy",
"EN12102_1_001",
"EN12102_1_002",
"EN14825_001",
"EN14825_002",
"EN14825_003",
"EN14825_004",
"EN14825_005",
"EN14825_008",
"EN14825_009",
"EN14825_010",
"EN14825_011",
"EN14825_012",
"EN14825_013",
"EN14825_014",
"EN14825_015",
"EN14825_016",
"EN14825_017",
"EN14825_018",
"EN14825_019",
"EN14825_020",
"EN14825_022",
"EN14825_023",
"EN14825_024",
"EN14825_025",
"EN14825_026",
"EN14825_027",
"EN14825_028",
"EN14825_030",
"EN14825_031",
"EN14825_032",
"EN14825_033",
"EN14825_034",
"EN14825_035",
"EN14825_036",
"EN14825_037",
"EN14825_038",
"EN14825_039",
]
general_info = [
"Manufacturer",
"Modelname",
"Date",
"Refrigerant",
"Mass of Refrigerant",
"Energy",
]
with os.scandir(THIS_FOLDER_PATH + "/../input/" + foldername) as dir1:
for file in dir1:
j = 0 # j: start index; i: end index
df = pd.read_csv(file)
try:
df.loc[df["varName"] == "title"]
except:
continue
try:
df.loc[df["varName"] == "application", "value"] = df.loc[
df["varName"] == "Type", "value"
].values[0]
except:
print(file)
for model in range(1, len(df.loc[df["varName"] == "title"]) + 1):
try:
i = df.loc[df["varName"] == "title"].index[model]
except:
i = len(df)
# df1 is dataframe of each model
df1 = df.iloc[j:i]
j = i
for temperature in ["low", "high"]:
# df2 is dataframe of each climate and temperature
df2 = df1.loc[(df1["climate"] >= 3) & (df1["climate"] <= 4)]
if temperature == "low":
df2.loc[df2["temperature"] == 4, "temperature"] = 6
df2 = df2.loc[(df2["temperature"] == 6)]
temperatures.append("low")
else:
df2.loc[(df2["temperature"] == 5), "temperature"] = 7
df2 = df2.loc[(df2["temperature"] == 7)]
temperatures.append("high")
for value, lst in zip(values, lists):
if value in general_info:
read_value = df.loc[df["varName"] == value, "value"].values[
0
]
elif (value == "title") or (value == "application"):
read_value = df1.loc[
df1["varName"] == value, "value"
].values[0]
else:
try:
read_value = df2.loc[
df2["varName"] == value, "value"
].values[0]
except:
read_value = ""
lst.append(read_value)
df_all["manufacturers"] = manufacturers
df_all["models"] = models
df_all["titels"] = titels
df_all["dates"] = dates
df_all["types"] = types
df_all["refrigerants"] = refrigerants
df_all["mass_of_refrigerants"] = mass_of_refrigerants
df_all["spl_indoor"] = spl_indoors
df_all["spl_outdoor"] = spl_outdoors
df_all["eta"] = eta
df_all["p_rated"] = p_rated
df_all["scop"] = scop
df_all["t_biv"] = t_biv
df_all["tol"] = tol
df_all["p_th_minus7"] = p_th_minus7
df_all["cop_minus7"] = cop_minus7
df_all["p_th_2"] = p_th_2
df_all["cop_2"] = cop_2
df_all["p_th_7"] = p_th_7
df_all["cop_7"] = cop_7
df_all["p_th_12"] = p_th_12
df_all["cop_12"] = cop_12
df_all["p_th_tbiv"] = p_th_tbiv
df_all["cop_tbiv"] = cop_tbiv
df_all["p_th_tol"] = p_th_tol
df_all["cop_tol"] = cop_tol
df_all["rated_airflows"] = rated_airflows
df_all["wtols"] = wtols
df_all["poffs"] = poffs
df_all["ptos"] = ptos
df_all["psbs"] = psbs
df_all["pcks"] = pcks
df_all["supply_energy"] = supply_energy
df_all["supp_energy_types"] = supp_energy_types
df_all["p_sups"] = p_sups
df_all["p_design_cools"] = p_design_cools
df_all["seers"] = seers
df_all["pdcs_35"] = pdcs_35
df_all["eer_35"] = eer_35
df_all["pdcs_30"] = pdcs_30
df_all["eer_30"] = eer_30
df_all["pdcs_25"] = pdcs_25
df_all["eer_25"] = eer_25
df_all["pdcs_20"] = pdcs_20
df_all["eer_20"] = eer_20
df_all["temperatures"] = temperatures
df_all.to_csv(r"../output/database.csv", index=False)
def reduce_heating_data():
# reduce the hplib_database_heating to a specific climate measurement series (average, warm, cold)
# delete redundant entries
# climate = average, warm or cold
df = pd.read_csv("../output/database.csv")
df = df.loc[df["eta"].isna() == 0]
# df=df.drop_duplicates(subset=['p_rated', 't_biv', 'tol', 'p_th_minus7', 'cop_minus7', 'p_th_2', 'cop_2',
# 'p_th_7', 'cop_7', 'p_th_12', 'cop_12', 'p_th_tbiv', 'cop_tbiv',
# 'p_th_tol', 'cop_tol', 'wtols', 'poffs', 'ptos',
# 'psbs', 'pcks', 'supp_energy_types', 'p_sups', 'p_design_cools',
# 'pdcs_35', 'eer_35', 'pdcs_30', 'eer_30', 'pdcs_25', 'eer_25',
# 'pdcs_20', 'eer_20', 'temperatures'])#types?
df.sort_values(
by=["manufacturers", "models"], inplace=True, key=lambda col: col.str.lower()
)
df = df.loc[df["temperatures"] == "low"].merge(
df.loc[df["temperatures"] == "high"], on="titels"
)
df = df[
[
"manufacturers_x",
"models_x",
"titels",
"dates_x",
"types_x",
"refrigerants_x",
"mass_of_refrigerants_x",
"spl_indoor_x",
"spl_outdoor_x",
"eta_x",
"p_rated_x",
"scop_x",
"t_biv_x",
"tol_x",
"p_th_minus7_x",
"cop_minus7_x",
"p_th_2_x",
"cop_2_x",
"p_th_7_x",
"cop_7_x",
"p_th_12_x",
"cop_12_x",
"p_th_tbiv_x",
"cop_tbiv_x",
"p_th_tol_x",
"cop_tol_x",
"rated_airflows_x",
"wtols_x",
"poffs_x",
"ptos_x",
"psbs_x",
"pcks_x",
"supp_energy_types_x",
"p_sups_x",
"p_design_cools_x",
"seers_x",
"pdcs_35_x",
"eer_35_x",
"pdcs_30_x",
"eer_30_x",
"pdcs_25_x",
"eer_25_x",
"pdcs_20_x",
"eer_20_x",
"spl_indoor_y",
"spl_outdoor_y",
"eta_y",
"p_rated_y",
"p_th_minus7_y",
"cop_minus7_y",
"p_th_2_y",
"cop_2_y",
"p_th_7_y",
"cop_7_y",
"p_th_12_y",
"cop_12_y",
"p_th_tbiv_y",
"cop_tbiv_y",
"p_th_tol_y",
"cop_tol_y",
"rated_airflows_y",
"p_sups_y",
"p_design_cools_y",
"seers_y",
"pdcs_35_y",
"eer_35_y",
"pdcs_30_y",
"eer_30_y",
"pdcs_25_y",
"eer_25_y",
"pdcs_20_y",
"eer_20_y",
]
]
df.rename(
columns={
"manufacturers_x": "manufacturers",
"models_x": "models",
"titels": "titel",
"dates_x": "dates",
"types_x": "types",
"refrigerants_x": "refrigerants",
"mass_of_refrigerants_x": "mass_of_refrigerants",
"spl_indoor_x": "spl_indoor_l",
"spl_outdoor_x": "spl_outdoor_l",
"eta_x": "eta_l",
"p_rated_x": "p_rated_l",
"scop_x": "scop_l",
"t_biv_x": "t_biv",
"tol_x": "tol",
"p_th_minus7_x": "p_th_minus7_l",
"cop_minus7_x": "cop_minus7_l",
"p_th_2_x": "p_th_2_l",
"cop_2_x": "cop_2_l",
"p_th_7_x": "p_th_7_l",
"cop_7_x": "cop_7_l",
"p_th_12_x": "p_th_12_l",
"cop_12_x": "cop_12_l",
"p_th_tbiv_x": "p_th_tbiv_l",
"cop_tbiv_x": "cop_tbiv_l",
"p_th_tol_x": "p_th_tol_l",
"cop_tol_x": "cop_tol_l",
"rated_airflows_x": "rated_airflows_l",
"wtols_x": "wtols",
"poffs_x": "poffs",
"ptos_x": "ptos",
"psbs_x": "psbs",
"pcks_x": "pcks",
"supp_energy_types_x": "supp_energy_types",
"p_sups_x": "p_sups_l",
"p_design_cools_x": "p_design_cools_l",
"seers_x": "seers_l",
"pdcs_35_x": "pdcs_35_l",
"eer_35_x": "eer_35_l",
"pdcs_30_x": "pdcs_30_l",
"eer_30_x": "eer_30_l",
"pdcs_25_x": "pdcs_25_l",
"eer_25_x": "eer_25_l",
"pdcs_20_x": "pdcs_20_l",
"eer_20_x": "eer_20_l",
"spl_indoor_y": "spl_indoor_h",
"spl_outdoor_y": "spl_outdoor_h",
"eta_y": "eta_h",
"p_rated_y": "p_rated_h",
"scop_y": "scop_h",
"p_th_minus7_y": "p_th_minus7_h",
"cop_minus7_y": "cop_minus7_h",
"p_th_2_y": "p_th_2_h",
"cop_2_y": "cop_2_h",
"p_th_7_y": "p_th_7_h",
"cop_7_y": "cop_7_h",
"p_th_12_y": "p_th_12_h",
"cop_12_y": "cop_12_h",
"p_th_tbiv_y": "p_th_tbiv_h",
"cop_tbiv_y": "cop_tbiv_h",
"p_th_tol_y": "p_th_tol_h",
"cop_tol_y": "cop_tol_h",
"rated_airflows_y": "rated_airflows_h",
"p_sups_y": "p_sups_h",
"p_design_cools_y": "p_design_cools_h",
"seers_y": "seers_h",
"pdcs_35_y": "pdcs_35_h",
"eer_35_y": "eer_35_h",
"pdcs_30_y": "pdcs_30_h",
"eer_30_y": "eer_30_h",
"pdcs_25_y": "pdcs_25_h",
"eer_25_y": "eer_25_h",
"pdcs_20_y": "pdcs_20_h",
"eer_20_y": "eer_20_h",
},
inplace=True,
)
df.to_csv("../output/database_reduced.csv", index=False)
def normalize_data():
df = pd.read_csv("../output/database_reduced.csv")
# change kW to W
df["p_th_minus7_l"] = (df["p_th_minus7_l"] * 1000).astype(int)
df["p_th_2_l"] = (df["p_th_2_l"] * 1000).astype(int)
df["p_th_7_l"] = (df["p_th_7_l"] * 1000).astype(int)
df["p_th_12_l"] = (df["p_th_12_l"] * 1000).astype(int)
df["p_th_tbiv_l"] = (df["p_th_tbiv_l"] * 1000).astype(int)
df["p_th_tol_l"] = (df["p_th_tol_l"] * 1000).astype(int)
df["p_th_minus7_h"] = (df["p_th_minus7_h"] * 1000).astype(int)
df["p_th_2_h"] = (df["p_th_2_h"] * 1000).astype(int)
df["p_th_7_h"] = (df["p_th_7_h"] * 1000).astype(int)
df["p_th_12_h"] = (df["p_th_12_h"] * 1000).astype(int)
df["p_th_tbiv_h"] = (df["p_th_tbiv_h"] * 1000).astype(int)
df["p_th_tol_h"] = (df["p_th_tol_h"] * 1000).astype(int)
df["pdcs_35_l"] = df["pdcs_35_l"] * 1000
df["pdcs_30_l"] = df["pdcs_30_l"] * 1000
df["pdcs_25_l"] = df["pdcs_25_l"] * 1000
df["pdcs_20_l"] = df["pdcs_20_l"] * 1000
df["pdcs_35_h"] = df["pdcs_35_h"] * 1000
df["pdcs_30_h"] = df["pdcs_30_h"] * 1000
df["pdcs_25_h"] = df["pdcs_25_h"] * 1000
df["pdcs_20_h"] = df["pdcs_20_h"] * 1000
# add P_el
df["p_el_minus7_l"] = (round(df["p_th_minus7_l"] / df["cop_minus7_l"])).astype(int)
df["p_el_2_l"] = (round(df["p_th_2_l"] / df["cop_2_l"])).astype(int)
df["p_el_7_l"] = (round(df["p_th_7_l"] / df["cop_7_l"])).astype(int)
df["p_el_12_l"] = (round(df["p_th_12_l"] / df["cop_12_l"])).astype(int)
df["p_el_tbiv_l"] = (round(df["p_th_tbiv_l"] / df["cop_tbiv_l"])).astype(int)
df["p_el_tol_l"] = (round(df["p_th_tol_l"] / df["cop_tol_l"])).astype(int)
df["p_el_minus7_h"] = (round(df["p_th_minus7_h"] / df["cop_minus7_h"])).astype(int)
df["p_el_2_h"] = (round(df["p_th_2_h"] / df["cop_2_h"])).astype(int)
df["p_el_7_h"] = (round(df["p_th_7_h"] / df["cop_7_h"])).astype(int)
df["p_el_12_h"] = (round(df["p_th_12_h"] / df["cop_12_h"])).astype(int)
df["p_el_tbiv_h"] = (round(df["p_th_tbiv_h"] / df["cop_tbiv_h"])).astype(int)
df["p_el_tol_h"] = (round(df["p_th_tol_h"] / df["cop_tol_h"])).astype(int)
df["p_el_35_l"] = round(df["pdcs_35_l"] / df["eer_35_l"])
df["p_el_30_l"] = round(df["pdcs_30_l"] / df["eer_30_l"])
df["p_el_25_l"] = round(df["pdcs_25_l"] / df["eer_25_l"])
df["p_el_20_l"] = round(df["pdcs_20_l"] / df["eer_20_l"])
df["p_el_35_h"] = round(df["pdcs_35_h"] / df["eer_35_h"])
df["p_el_30_h"] = round(df["pdcs_30_h"] / df["eer_30_h"])
df["p_el_25_h"] = round(df["pdcs_25_h"] / df["eer_25_h"])
df["p_el_20_h"] = round(df["pdcs_20_h"] / df["eer_20_h"])
df["p_el_minus7_l_n"] = df["p_el_minus7_l"] / df["p_el_minus7_h"]
df["p_el_2_l_n"] = df["p_el_2_l"] / df["p_el_minus7_h"]
df["p_el_7_l_n"] = df["p_el_7_l"] / df["p_el_minus7_h"]
df["p_el_12_l_n"] = df["p_el_12_l"] / df["p_el_minus7_h"]
df["p_el_tbiv_l_n"] = df["p_el_tbiv_l"] / df["p_el_minus7_h"]
df["p_el_tol_l_n"] = df["p_el_tol_l"] / df["p_el_minus7_h"]
df["p_el_35_l_n"] = df["p_el_35_l"] / df["p_el_35_l"]
df["p_el_30_l_n"] = df["p_el_30_l"] / df["p_el_35_l"]
df["p_el_25_l_n"] = df["p_el_25_l"] / df["p_el_35_l"]
df["p_el_20_l_n"] = df["p_el_20_l"] / df["p_el_35_l"]
df["p_el_minus7_h_n"] = df["p_el_minus7_h"] / df["p_el_minus7_h"]
df["p_el_2_h_n"] = df["p_el_2_h"] / df["p_el_minus7_h"]
df["p_el_7_h_n"] = df["p_el_7_h"] / df["p_el_minus7_h"]
df["p_el_12_h_n"] = df["p_el_12_h"] / df["p_el_minus7_h"]
df["p_el_tbiv_h_n"] = df["p_el_tbiv_h"] / df["p_el_minus7_h"]
df["p_el_tol_h_n"] = df["p_el_tol_h"] / df["p_el_minus7_h"]
df["p_el_35_h_n"] = df["p_el_35_h"] / df["p_el_35_l"]
df["p_el_30_h_n"] = df["p_el_30_h"] / df["p_el_35_l"]
df["p_el_25_h_n"] = df["p_el_25_h"] / df["p_el_35_l"]
df["p_el_20_h_n"] = df["p_el_20_h"] / df["p_el_35_l"]
df = df.loc[df["cop_minus7_l"] != 1]
df = df[
[
"manufacturers",
"models",
"titel",
"dates",
"types",
"refrigerants",
"mass_of_refrigerants",
"spl_indoor_l",
"spl_outdoor_l",
"eta_l",
"p_rated_l",
"scop_l",
"t_biv",
"tol",
"p_th_minus7_l",
"cop_minus7_l",
"p_el_minus7_l",
"p_th_2_l",
"cop_2_l",
"p_el_2_l",
"p_th_7_l",
"cop_7_l",
"p_el_7_l",
"p_th_12_l",
"cop_12_l",
"p_el_12_l",
"p_th_tbiv_l",
"cop_tbiv_l",
"p_el_tbiv_l",
"p_th_tol_l",
"cop_tol_l",
"p_el_tol_l",
"rated_airflows_l",
"wtols",
"poffs",
"ptos",
"psbs",
"pcks",
"supp_energy_types",
"p_sups_l",
"p_design_cools_l",
"seers_l",
"pdcs_35_l",
"eer_35_l",
"p_el_35_l",
"pdcs_30_l",
"eer_30_l",
"p_el_30_l",
"pdcs_25_l",
"eer_25_l",
"p_el_25_l",
"pdcs_20_l",
"eer_20_l",
"p_el_20_l",
"spl_indoor_h",
"spl_outdoor_h",
"eta_h",
"p_rated_h",
"p_th_minus7_h",
"cop_minus7_h",
"p_el_minus7_h",
"p_th_2_h",
"cop_2_h",
"p_el_2_h",
"p_th_7_h",
"cop_7_h",
"p_el_7_h",
"p_th_12_h",
"cop_12_h",
"p_el_12_h",
"p_th_tbiv_h",
"cop_tbiv_h",
"p_el_tbiv_h",
"p_th_tol_h",
"cop_tol_h",
"p_el_tol_h",
"rated_airflows_h",
"p_sups_h",
"p_design_cools_h",
"seers_h",
"pdcs_35_h",
"eer_35_h",
"p_el_35_h",
"pdcs_30_h",
"eer_30_h",
"p_el_30_h",
"pdcs_25_h",
"eer_25_h",
"p_el_25_h",
"pdcs_20_h",
"eer_20_h",
"p_el_20_h",
"p_el_minus7_l_n",
"p_el_2_l_n",
"p_el_7_l_n",
"p_el_12_l_n",
"p_el_tbiv_l_n",
"p_el_tol_l_n",
"p_el_35_l_n",
"p_el_30_l_n",
"p_el_25_l_n",
"p_el_20_l_n",
"p_el_minus7_h_n",
"p_el_2_h_n",
"p_el_7_h_n",
"p_el_12_h_n",
"p_el_tbiv_h_n",
"p_el_tol_h_n",
"p_el_35_h_n",
"p_el_30_h_n",
"p_el_25_h_n",
"p_el_20_h_n",
]
]
# add second (+18/+23 °C) point for cooling based on other heat pumps
"""Overall there are not so many unique Keymark heat pumps for cooling in comparison to heating.
With our fit method it is not possible to fit only over one outflow temperature. For that reason we added another
set point at 18°C output temperature based on the heat pumps we had with this condition in Keymark. For that purpose, we identified multiplication factors for eletrical power and eer between 7°C and 18°C secondary output temperature. The mean value of that are used to calculate the electrical power and EER at 18°C for other heat pumps:
P_el at 18°C = P_el at 7°C * multiplication factor
EER at 18°C = EER at 7°C * multiplication factor"""
df.loc[(df["eer_20_l"].isna() == 0) & (df["p_el_35_h_n"].isna()), "eer_20_h"] = (
df.loc[(df["eer_20_l"].isna() == 0) & (df["p_el_35_h_n"].isna()), "eer_20_l"]
* (
df[df["p_el_35_h_n"].isna() == 0]["eer_20_h"]
/ df[df["p_el_35_h_n"].isna() == 0]["eer_20_l"]
)
.unique()
.mean()
)
df.loc[(df["eer_25_l"].isna() == 0) & (df["p_el_35_h_n"].isna()), "eer_25_h"] = (
df.loc[(df["eer_25_l"].isna() == 0) & (df["p_el_35_h_n"].isna()), "eer_25_l"]
* (
df[df["p_el_35_h_n"].isna() == 0]["eer_25_h"]
/ df[df["p_el_35_h_n"].isna() == 0]["eer_25_l"]
)
.unique()
.mean()
)
df.loc[(df["eer_30_l"].isna() == 0) & (df["p_el_35_h_n"].isna()), "eer_30_h"] = (
df.loc[(df["eer_30_l"].isna() == 0) & (df["p_el_35_h_n"].isna()), "eer_30_l"]
* (
df[df["p_el_35_h_n"].isna() == 0]["eer_30_h"]
/ df[df["p_el_35_h_n"].isna() == 0]["eer_30_l"]
)
.unique()
.mean()
)
df.loc[(df["eer_35_l"].isna() == 0) & (df["p_el_35_h_n"].isna()), "eer_35_h"] = (
df.loc[(df["eer_35_l"].isna() == 0) & (df["p_el_35_h_n"].isna()), "eer_35_l"]
* (
df[df["p_el_35_h_n"].isna() == 0]["eer_35_h"]
/ df[df["p_el_35_h_n"].isna() == 0]["eer_35_l"]
)
.unique()
.mean()
)
df.loc[
(df["p_el_20_l_n"].isna() == 0) & (df["p_el_35_h_n"].isna()), "p_el_20_h_n"
] = (df[df["p_el_35_h_n"].isna() == 0]["p_el_20_h_n"].unique().mean())
df.loc[
(df["p_el_25_l_n"].isna() == 0) & (df["p_el_35_h_n"].isna()), "p_el_25_h_n"
] = (df[df["p_el_35_h_n"].isna() == 0]["p_el_25_h_n"].unique().mean())
df.loc[
(df["p_el_30_l_n"].isna() == 0) & (df["p_el_35_h_n"].isna()), "p_el_30_h_n"
] = (df[df["p_el_35_h_n"].isna() == 0]["p_el_30_h_n"].unique().mean())
df.loc[
(df["p_el_35_l_n"].isna() == 0) & (df["p_el_35_h_n"].isna()), "p_el_35_h_n"
] = (df[df["p_el_35_h_n"].isna() == 0]["p_el_35_h_n"].unique().mean())
df.to_csv(
r"../output/database_reduced_normalized.csv", encoding="utf-8", index=False
)
def identify_subtypes():
# Identify Subtype like On-Off or Regulated by comparing the thermal Power output at different temperature levels:
# -7/34 | 2/30 | 7/27 | 12/24
# assumptions for On-Off Heatpump: if temperature difference is bigger, thermal Power output is smaller
# assumptions for Regulated: everythin else
df = pd.read_csv(r"../output/database_reduced_normalized.csv")
df["Subtype"] = np.where(
(df["p_th_minus7_l"] <= df["p_th_2_l"])
& (df["p_th_2_l"] <= df["p_th_7_l"])
& (df["p_th_7_l"] <= df["p_th_12_l"]),
"On-Off",
"Regulated",
)
filt1 = (df["types"] == "Outdoor Air/Water") & (df["Subtype"] == "Regulated")
df.loc[filt1, "Group"] = 1
filt1 = (df["types"] == "Exhaust Air/Water") & (df["Subtype"] == "Regulated")
df.loc[filt1, "Group"] = 7
filt1 = (df["types"] == "Brine/Water") & (df["Subtype"] == "Regulated")
df.loc[filt1, "Group"] = 2
filt1 = (df["types"] == "Brine/Water and Water/Water") & (
df["Subtype"] == "Regulated"
)
df.loc[filt1, "Group"] = 2
filt1 = (df["types"] == "Water/Water") & (df["Subtype"] == "Regulated")
df.loc[filt1, "Group"] = 3
filt1 = (df["types"] == "Outdoor Air/Water") & (df["Subtype"] == "On-Off")
df.loc[filt1, "Group"] = 4
filt1 = (df["types"] == "Exhaust Air/Water") & (df["Subtype"] == "On-Off")
df.loc[filt1, "Group"] = 7
filt1 = (df["types"] == "Brine/Water") & (df["Subtype"] == "On-Off")
df.loc[filt1, "Group"] = 5
filt1 = (df["types"] == "Brine/Water and Water/Water") & (df["Subtype"] == "On-Off")
df.loc[filt1, "Group"] = 5
filt1 = (df["types"] == "Water/Water") & (df["Subtype"] == "On-Off")
df.loc[filt1, "Group"] = 6
df = df.loc[df["Group"] != 7]
df.to_csv(
r"../output/database_reduced_normalized_subtypes.csv",
encoding="utf-8",
index=False,
)
def fit_simple(w, x, y, z):
p0 = [0.1, 0.001, 0.1, 1.0] # starting values
a = (w, x, y, z)
para, _ = scipy.optimize.leastsq(func_simple_zero, p0, args=a)
return para
def func_simple_zero(para, w, x, y, z):
k1, k2, k3, k4 = para
z_calc = k1 * w + k2 * x + k3 + k4 * y
z_diff = z_calc - z
return z_diff
def func_simple(para, w, x, y):
# Function to calculate z using parameters and any x and y:
k1, k2, k3, k4 = para
z = k1 * w + k2 * x + k3 + k4 * y
return z
def interpolate_t_out(t_in):
# Function to interpolate Temperature out at T_biv and TOL
t_amb = [12, 7, 2, -7]
t_out_low = [24, 27, 30, 34]
t_out_high = [30, 36, 42, 52]
if t_in >= 7:
i = 0
elif t_in <= 2:
i = 2
else:
i = 1
t_out_l = t_out_low[i + 1] - (t_out_low[i + 1] - t_out_low[i]) * (
t_in - t_amb[i + 1]
) / (t_amb[i] - t_amb[i + 1])
t_out_h = t_out_high[i + 1] - (t_out_high[i + 1] - t_out_high[i]) * (
t_in - t_amb[i + 1]
) / (t_amb[i] - t_amb[i + 1])
return round(t_out_l, 2), round(t_out_h, 2)
def calculate_fitting_parameters():
# Calculate function parameters from normalized values
p1_P_el = []
p2_P_el = []
p3_P_el = []
p4_P_el = []
p1_COP = []
p2_COP = []
p3_COP = []
p4_COP = []
p1_P_el_c = []
p2_P_el_c = []
p3_P_el_c = []
p4_P_el_c = []
p1_EER = []
p2_EER = []
p3_EER = []
p4_EER = []
df = pd.read_csv(r"../output/database_reduced_normalized_subtypes.csv")
for model in df["titel"]:
group = df.loc[df["titel"] == (model), "Group"].values[0]
# Create Model DF with all important information for fitting heating parameters
df_model = pd.DataFrame()
df_model["T_amb"] = [-7, -7, 2, 2, 7, 7, 12, 12]
df_model["T_out"] = [34, 52, 30, 42, 27, 36, 24, 30]
df_model["P_el"] = [
(df.loc[df["titel"] == (model), "p_el_minus7_l_n"].values[0]),
(df.loc[df["titel"] == (model), "p_el_minus7_h_n"].values[0]),
(df.loc[df["titel"] == (model), "p_el_2_l_n"].values[0]),
(df.loc[df["titel"] == (model), "p_el_2_h_n"].values[0]),
(df.loc[df["titel"] == (model), "p_el_7_l_n"].values[0]),
(df.loc[df["titel"] == (model), "p_el_7_h_n"].values[0]),
(df.loc[df["titel"] == (model), "p_el_12_l_n"].values[0]),
(df.loc[df["titel"] == (model), "p_el_12_h_n"].values[0]),
]
df_model["COP"] = [
(df.loc[df["titel"] == (model), "cop_minus7_l"].values[0]),
(df.loc[df["titel"] == (model), "cop_minus7_h"].values[0]),
(df.loc[df["titel"] == (model), "cop_2_l"].values[0]),
(df.loc[df["titel"] == (model), "cop_2_h"].values[0]),
(df.loc[df["titel"] == (model), "cop_7_l"].values[0]),
(df.loc[df["titel"] == (model), "cop_7_h"].values[0]),
(df.loc[df["titel"] == (model), "cop_12_l"].values[0]),
(df.loc[df["titel"] == (model), "cop_12_h"].values[0]),
]
if group == 1 or group == 4: # Air/Water
df_model["T_in"] = [-7, -7, 2, 2, 7, 7, 12, 12]
elif group == 2 or group == 5: # Brine/Water
df_model["T_in"] = [0, 0, 0, 0, 0, 0, 0, 0]
df_model1 = df_model.copy()
df_model1["T_in"] = df_model1["T_in"] + 1
df_model1["T_out"] = df_model1["T_out"] + 1
df_model = pd.concat([df_model, df_model1])
else: # Water/Water
df_model["T_in"] = [10, 10, 10, 10, 10, 10, 10, 10]
df_model1 = df_model.copy()
df_model1["T_in"] = df_model1["T_in"] + 1
df_model1["T_out"] = df_model1["T_out"] + 1
df_model = pd.concat([df_model, df_model1])
"""
t_biv=df.loc[df['titel']==(model),'t_biv'].values[0]
tol=df.loc[df['titel']==(model),'tol'].values[0]
tol_low,tol_high=interpolate_t_out(tol)
t_biv_low,t_biv_high=interpolate_t_out(t_biv)
df_model['T_amb']=[tol,tol,t_biv,t_biv,-7,-7,2,2,7,7,12,12]
df_model['T_out']=[tol_low,tol_high,t_biv_low,t_biv_high,34,52,30,42,27,36,24,30]
df_model['P_el']=[(df.loc[df['titel']==(model),'p_el_tol_l_n'].values[0]),
(df.loc[df['titel']==(model),'p_el_tol_h_n'].values[0]),
(df.loc[df['titel']==(model),'p_el_tbiv_l_n'].values[0]),
(df.loc[df['titel']==(model),'p_el_tbiv_h_n'].values[0]),
(df.loc[df['titel']==(model),'p_el_minus7_l_n'].values[0]),