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EnergySystemModels-0.1.17.post63
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مشاهده بیشترتوضیحات
Energy systems models are the mathematical models that are developed in order to represent as reliably as possible various energy-related problems.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | EnergySystemModels-0.1.17.post63 |
| نام | EnergySystemModels |
| نسخه کتابخانه | 0.1.17.post63 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Zoheir HADID |
| ایمیل نویسنده | zoheir.hadid@gmail.com |
| آدرس صفحه اصلی | https://github.com/ZoheirHADID/EnergySystemModels |
| آدرس اینترنتی | https://pypi.org/project/EnergySystemModels/ |
| مجوز | - |
- [1. Thermodynamic Cycles Package](#1-thermodynamic-cycles-package)
* [1.1. Fluid Source](#11-fluid-source)
+ [1.1.1. Input parameters](#111-Input-parameters)
* [1.2. Sink](#12-sink)
* [1.3. Compressor](#13-compressor)
+ [1.3.1. Compressor model](#131-compressor-model)
* [1.4. Water Heat Storage](#14-water-heat-storage)
+ [1.4.1. Mixed Tank](#141-mixed-tank)
- [2. AHU modules](#2-ahu-modules)
* [2.1 Fresh AHU Example](#21-fresh-ahu-exemple)
- [3. Chiller Example](#3-Chiller-Example)
* [3.1. Launch Chiller Application (Tkinter GUI)](#31-Launch-Chiller-Application-(Tkinter-GUI))
* [3.2. Create Oriented-Object Chiller](#32-Create-Oriented-Object-Chiller)
- [4. Données météo](#4-Données-météo)
* [4.1. OpenWeaterMap](#41-OpenWeaterMap)
* [4.2. MeteoCiel](#42-MeteoCiel)
- [5. IPMVP](#5-IPMVP)
# 1. Thermodynamic Cycles Package
## 1.1. Fluid Source
### 1.1.1. Input parameters
| Symbol | Description | SI Units | Used Units |
|----------|:-------------:|------:|------:|
| Ti_degC | Inlet temerature | K | °C |
| fluid | Fluid/Refrigerant name | String |"air","ammonia","R134a",...|
| F_kgs, F_Sm3s, F_m3s, F_Sm3h, F_m3h, F_kgh | Input Flow rate | kg/s | kg/s, Sm3/s, m3/s, Sm3/h, m3/h, kg/h |
| Pi_bar | Inlet Pressure | Pa | bara |
``` python
from ThermodynamicCycles.Source import Source
#Create Compressor Object
SOURCE=Source.Object()
#Data Input
SOURCE.Pi_bar=1.01325
SOURCE.fluid="air"
SOURCE.F_kgs=1
#SOURCE.F_Sm3s=2937.482966/3600 #SOURCE.F_m3s=2480.143675/3600
#SOURCE.F_Sm3h=1 #SOURCE.F_m3h=2480.143675 #SOURCE.F_kgh=3600
#Calculate Object
SOURCE.calculate()
#Data output
print(SOURCE.df)
```
## 1.2. Sink
### 1.2.1. Test Sink
``` python
from ThermodynamicCycles.Sink import Sink
#from ThermodynamicCycles.Connect import Fluid_connect
#Create Sink object
SINK=Sink.Object()
#Fluid_connect(SINK.Inlet,SOURCE.Outlet)
SINK.Inlet.fluid="air"
SINK.Inlet.F_kgs=0.334
SINK.Inlet.P=101325
SINK.Inlet.h=420000
#calculate SINK
SINK.calculate()
#Print result
print(SINK.df)
print(SINK.To_degC)
```
### Output data
## 1.3. Compressor
### 1.3.1. Compressor model
```python
from ThermodynamicCycles.Source import Source
from ThermodynamicCycles.Compressor import Compressor
from ThermodynamicCycles.Sink import Sink
from ThermodynamicCycles.Connect import Fluid_connect
#Create Compressor Object with Source and fluid Sink
SOURCE=Source.Object()
COMP=Compressor.Object()
SINK=Sink.Object()
#Data Input
SOURCE.Ti_degC=20
SOURCE.fluid="air"
SOURCE.Pi_bar=1
SOURCE.F_Sm3h=500 # is not considered if COMP.Qcomp is not None
COMP.eta_is=0.80
COMP.Tdischarge_target=80 # (discharge temperature in degC, after cooler)
COMP.HP=7.5*100000 # discharge pressure in Pa
COMP.Qcomp=48745.761 # if Energy Power is given (W) the Mass flow rate is recalculated
#Calculate and Connect Objects
SOURCE.calculate()
Fluid_connect(COMP.Inlet,SOURCE.Outlet)
COMP.calculate()
Fluid_connect(SINK.Inlet,COMP.Outlet)
SINK.calculate()
#Data output (print DataFrame)
print(SOURCE.df)
print(COMP.df)
print(SINK.df)
```
<img src="https://render.githubusercontent.com/render/math?math=\eta_{is} = 0.8">
# EnergySystemModels
Energy System Models for Energy Efficiency Calculation
## 1.4. Water Heat Storage
### 1.4.1. Mixed Tank
```python
from ThermodynamicCycles import MixedStorage
from ThermodynamicCycles.Source import Source
from ThermodynamicCycles.Sink import Sink
from ThermodynamicCycles.Connect import Fluid_connect
#lecture d'un fichier excel
#pip install pandas
import pandas as pd
import os
data=pd.read_excel( os.path.join(os.path.dirname(__file__), 'HotWaterStorage.xlsx'))
data['Timestamp'] = pd.to_datetime(data['Timestamp'], unit="%d/%m/%y %H:%M:%S")
rows = data.shape[0]
print(rows)
print(data.columns)
#initialiser les table de sortie
df_result=pd.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False)
df_source=pd.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False)
df_str=pd.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False)
df_sink=pd.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False)
#CreateTank Object with Source and fluid Sink
SOURCE=Source.Object()
SINK=Sink.Object()
STR=MixedStorage.Object()
#paramètres
STR.V=4
STR.Tinit_degC=40
STR.t=1*3600 #in seconde
for r in range(1, rows):
#Data Input
SOURCE.Ti_degC=data["TdegC"][r]
SOURCE.fluid="water"
SOURCE.Pi_bar=1
SOURCE.F_m3h=data["F_m3h"][r]
SOURCE.Timestamp=data["Timestamp"][r]
STR.Timestamp=data["Timestamp"][r]
SINK.Timestamp=data["Timestamp"][r]
#calcul du pas de temps
Timestamp=data["Timestamp"][r]
dt=(data["Timestamp"][r]-data["Timestamp"][r-1]).total_seconds()
#print(dt)
STR.t=dt
SOURCE.calculate()
Fluid_connect(STR.Inlet,SOURCE.Outlet)
STR.calculate()
Fluid_connect(SINK.Inlet,STR.Outlet)
SINK.calculate()
df_str=df_str.append(STR.df.T)
df_source=df_source.append(SOURCE.df.T)
df_sink=df_sink.append(SINK.df.T)
# Add new column to the DataFrame
df_result=df_str.merge(df_sink, on=['Timestamp']).merge(df_source, on=['Timestamp'])
print(df_result)
with pd.ExcelWriter('output_WaterStorage.xlsx') as writer: #Création d'un fichier de sortie + Ecriture
df_result.to_excel(writer, sheet_name='Feuille output',index=False)
data.to_excel(writer, sheet_name='Feuille input',index=False)
####PLOT#####
# Import Library
import matplotlib.pyplot as plt
df_result.index=df_result['Timestamp']
# to set the plot size
plt.figure(figsize=(16, 8), dpi=100)
# Plot
df_result["str_Ti_degC"].plot(marker="o",label='Tentrèe (°C)', color='orange')
df_result["str_T_degC"].plot(marker="o",label='Tsortie (°C)')
df_result["cumul_Qstr_kWh"].plot(marker="o",label='Energie stockée cumulée (kWh)')
df_result["Qstr_kW"].plot(marker="o",label='Puissance de stockage (kW)')
# Labelling
plt.xlabel("Date")
plt.ylabel("kWh, kW et °C")
plt.legend()
plt.grid()
plt.title("Stockage d'énergie thermique")
# Display
plt.show()
```
# 2. AHU modules
# 2.1 Fresh AHU Example
``` python
# =============================================================================
# AHU Model (Fresh air + Heating Coil + humidifier)
# =============================================================================
#module de calcul des prop d'air humide
from AHU import FreshAir
#Heating Coil Component
from AHU import HeatingCoil
#composant Humidifier (vapeur ou adiabatique)
from AHU.Humidification import Humidifier
# connexion entre les composants
from AHU.Connect import Air_connect
##########Création des Objects
AN=FreshAir.Object()
BC=HeatingCoil.Object()
HMD=Humidifier.Object()
#Récupération des données entrées par l'utilisateur
#AN
AN.m_vol=3000 #m3/h
#print("AN.m_vol = ",AN.m_vol)
AN.T=14 #°C
AN.HR_FreshAir=71 # %
#BC
BC.T_out_target=15 #°C
#Humidifier
HMD.HA_out_target=8 #g/Kg dry air
#calculate les propriétés d'air neuf; !important
AN.calculate()
Air_connect(BC.Inlet,AN.Outlet)
BC.calculate()
Air_connect(HMD.Inlet,BC.Outlet)
HMD.HumidType="vapeur" #par default : Humdificateur adiabatique
HMD.calculate()
#enregistrer les résultats du module d'air neuf
#Absolute Humidity g/kg_as
print("Fresh Air Absolute Humidity g/kg_as",round(AN.HA,1))
# print("HA_FreshAir[r-1] = ",HA_FreshAir[r-1])
#Sat Vapor Pressure " Pa"
print("Fresh Air Sat Vapor Pressure Pa",round(AN.Pvsat,0))
#Wet-Bulb Temperature °C
print("Fresh Air Wet-Bulb Temperature °C",round(AN.T_hum,1))
#Specific Enthalpy KJ/Kg_as
print("Fresh Air Specific Enthalpy KJ/Kg_as",round(AN.h,3))
#enregistrer les résultats de la Coil de préchauffage
# Specific Enthalpy KJ/Kg_as
print("Heating Coil Specific Enthalpy KJ/Kg_as",round(BC.h_out,1))
# Thermal Power kW"
print("Heating Coil Thermal Power kW",round(BC.Qth,1))
# Relative Humidity %"
print("Heating Coil Relative Humidity %",round(BC.HR_out,1))
print("Humidifier Steam mass flow rate Kg/s",round(HMD.m_water,3))
print("Humidifier Dry air mass flow rate Kg/s",round(HMD.m_as,3))
# =============================================================================
# End AHU Model
# =============================================================================
```
# 3. Chiller Example
## 3.1. Launch Chiller Application (Tkinter GUI)
``` python
from TkinterGUI import Chiller
```
## 3.2. Create Oriented-Object Chiller
``` python
# =============================================================================
# Chiller Model (Evaporator + Compressor + Desuperheater + Condenser + Expansion_Valve)
# =============================================================================
# #ThermodynamicCycles
import CoolProp.CoolProp as CP
from ThermodynamicCycles.Evaporator import Evaporator
from ThermodynamicCycles.Compressor import Compressor
from ThermodynamicCycles.Desuperheater import Desuperheater
from ThermodynamicCycles.Expansion_Valve import Expansion_Valve
from ThermodynamicCycles.Condenser import Condenser
from ThermodynamicCycles.Connect import Fluid_connect
###############Create chiller component object ##################
EVAP=Evaporator.Object()
COMP=Compressor.Object()
DESURCH=Desuperheater.Object()
COND=Condenser.Object()
DET=Expansion_Valve.Object()
###############################################################
########################Cycle Inlet Parameters########################
#***************Evaporator parameters*******
fluid="R134a"
EVAP.fluid=fluid
EVAP.Inlet.F_kgs=1 #Kg/s
# T or P evap :
EVAP.LP_bar=2.930154 #bar
#EVAP.Ti_degC=0 #Tevap
EVAP.surchauff=2 #superheating
EVAP.Inlet.h= CP.PropsSI('H','P',1*1e5,'T',40+273.15,fluid) #initialisation pour le calcul en boucle
#******************compresseur parameters***********
# give HP or Tcond
#COMP.HP=1e5*10 #Pa
COMP.Tcond_degC=40
COMP.eta_is=0.8 # isentropic efficiency
COMP.Tdischarge_target=80 #°C compressor outlet temperature, neglected if compressor is not cooled
COMP.Qcomp==100000 #in (W) If this value is given, the mass flow rate is calculated /Write None if not used #in (W) If this value is given, the mass flow rate is calculated
#*************** Condenser parameters**************
COND.subcooling=2 #°C subcooling
#calculation algorithme
EVAP.calculate() # evaporator initialisation
Fluid_connect(COMP.Inlet,EVAP.Outlet)
COMP.calculate()
Fluid_connect(DESURCH.Inlet,COMP.Outlet)
DESURCH.calculate()
Fluid_connect(COND.Inlet, DESURCH.Outlet)
COND.calculate()
Fluid_connect(DET.Inlet,COND.Outlet)
Fluid_connect(DET.Outlet,EVAP.Inlet)
DET.calculate()
Fluid_connect(EVAP.Inlet,DET.Outlet)
EVAP.calculate() # recalculate evaporator
# Cycle performance
EER=EVAP.Qevap/COMP.Qcomp
print("EER="+str(round(EER,1))+" ")
QcondTot=COND.Qcond+DESURCH.Qdesurch
print("QcondTot="+str(round(QcondTot/1000,1))+" kW")
COP=QcondTot/COMP.Qcomp
print("COP="+str(round(COP,1))+" ")
# ####### Print Results#######################"
print(COMP.df)
print(EVAP.df)
print(DESURCH.df)
print(COND.df)
print(DET.df)
# =============================================================================
# End Chiller Model
# =============================================================================
```
# 3. Pinch Analysis
``` python
from PinchAnalysis.PinchCalculation import PinchCalculation
import pandas as pd
import matplotlib.pyplot as plt
#DataFrame Input Data
df=pd.DataFrame({'id': [1, 2, 3, 4],
'name': ['stream1', 'stream2', 'stream3', 'stream4'],
'Ti': [200, 50, 125, 45],
'To': [50, 250, 124, 195],
'mCp': [3, 2,300,4],
'dTmin2': [5, 5, 10, 10],
'integration': [True, True, True, True]
})
#Pinch Calculation
T, plot_GCC, plot_ccf,plot_ccc,utilite_froide,utilite_chaude=PinchCalculation(df)
#Print the results
print("T",T)
print("GCC",plot_GCC[:,0])
print("ccf",plot_ccf[:,0])
print("ccc",plot_ccc[:,0])
print("utilite_froide",utilite_froide)
print("uilite_chaude",utilite_chaude)
# Plot the results
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.plot(plot_ccf[:,0],T, color='tab:blue')
ax1.plot(plot_ccc[:,0],T, color='tab:red')
ax2.plot(plot_GCC[:,0],T, color='tab:orange')
ax1.set(xlabel='kW', ylabel='Temperature (°C)')
ax2.set(xlabel='kW')
ax1.grid(True)
ax2.grid(True)
plt.show()
```
# 4 Données-météo
## 4.1 OpenWeaterMap
Pour lire la température et l'humidité en fonction d'une position GPS à partir des données de OpenWeaterMap
``` python
from OpenWeatherMap import OpenWeatherMap_call_location
df=OpenWeatherMap_call_location.API_call_location("48.862725","2.287592")
print(df)
```
<Response [200]>
Timestamp T(°C) HR(%)
0 2023-03-09 14:09:01.941527 14.39 72
## 4.2 MeteoCiel
Scraper les données historique d'une station Meteociel
``` python
from datetime import datetime
#pip install energysystemmodels
from MeteoCiel.MeteoCiel_Scraping import MeteoCiel_histoScraping
# 0. Données d'entrée#
code2=7150 #station météo Le Bourget (93) 7150 pour l'adresse : 93140 Bondy
date_debut=datetime(2018,1,1)
date_fin=datetime.now()
print("date_debut:",date_debut)
print("date_fin:",date_fin)
# 1. Utiliser la fonction de scraping
df_histo,df_day, df_month, df_year=MeteoCiel_histoScraping(code2,date_debut,date_fin)
# imprimer l'historique avec le calcul des DJU_chaud
print(df_histo,df_day, df_month, df_year)
# 2. Entregistrer sous forme d'un fichier Excel
df_histo.to_excel("Meteociel.fr_station_"+str(date_debut.date())+"_to_"+str(date_fin.date())+".xlsx")
```
# 5-IPMVP
``` python
#pip install energysystemmodels
from IPMVP.IPMVP import Mathematical_Models
import os
from datetime import datetime
import pandas as pd
# Nom du fichier de données
filename="Input-Data.xlsx"
# Nom de la colonne de Date
date="Timestamp"
#lecture des données
directory = os.getcwd()
file_directory=directory+"\\"+filename
df=pd.read_excel(file_directory)
#Transformer la date en index de date horaire.
df[date] = pd.to_datetime(df[date])
df = df.set_index(df[date])
# définition des périodes de référence et de suivi
start_baseline_period=datetime(2018,1,1,hour=0)
end_baseline_period=datetime(2021,12,31,hour=0)
start_reporting_period=datetime(2022,1,1,hour=0)
end_reporting_period=datetime(2023,3,1,hour=0)
# Modèle IPMVP (Maille journalière)
df = df.resample('D').sum()
X=df[["x1","x2","x3","x4","x5","x6"]] #variables explicatives candidates
y=df["y"] # consommation d'énergie
day_model=Mathematical_Models(y,X,start_baseline_period,end_baseline_period,start_reporting_period,end_reporting_period,degree=3,print_report=True,seuil_z_scores=3)
# Modèle IPMVP Maille hébdo
weekly_X = X.resample('W').sum()
weekly_y =y.resample('W').sum()
day_model=Mathematical_Models(weekly_y,weekly_X,start_baseline_period,end_baseline_period,start_reporting_period,end_reporting_period)
# Modèle IPMVP Maille Mensuelle
monthly_X = X.resample('M').sum()
monthly_y =y.resample('M').sum()
month_model=Mathematical_Models(monthly_y,monthly_X,start_baseline_period,end_baseline_period,start_reporting_period,end_reporting_period)
# Modèle IPMVP Maille annuelle
# yearly_X = X.resample('Y').sum()
# yearly_y =y.resample('Y').sum()
# year_model=Mathematical_Models(yearly_y,yearly_X,start_baseline_period,end_baseline_period,start_reporting_period,end_reporting_period)
```
نیازمندی
| مقدار | نام |
|---|---|
| - | pandas |
| ~=6.4.1 | CoolProp |
| ~=1.3.2 | pylab-sdk |
| - | matplotlib |
| ~=1.3.3 | tkintertable |
| - | numpy |
| - | pyqtgraph |
| - | thermochem |
| - | sip |
| - | PyQt-builder |
| - | PyQt5 |
| - | scikit-learn |
| - | beautifulsoup4 |
| - | tqdm |
| - | statsmodels |
| - | python-docx |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl EnergySystemModels-0.1.17.post63:
pip install EnergySystemModels-0.1.17.post63.whl
نصب پکیج tar.gz EnergySystemModels-0.1.17.post63:
pip install EnergySystemModels-0.1.17.post63.tar.gz