energysystemmodels 20260501002

Energy systems models are the mathematical models that are developed in order to represent as reliably as possible various energy-related problems.

# EnergySystemModels

Energy systems models are mathematical models developed to reliably represent various energy-related problems. These models can be used for simulation, optimization, and analysis of energy systems to improve efficiency, reduce costs, minimize environmental impact, and achieve energy savings.

Les modeles de systemes energetiques sont des modeles mathematiques developpes pour representer de maniere aussi fiable que possible divers problemes lies a l''energie. Ces modeles peuvent etre utilises pour la simulation, l''optimisation et l''analyse des systemes energetiques afin d''ameliorer l''efficacite, de reduire les couts et de minimiser l''impact environnemental, ainsi que pour realiser des economies d''energie.

Documentation

• FR Documentation francaise
• EN English documentation

Installation

pip install energysystemmodels

Modules disponibles / Available Modules

Cycles Thermodynamiques / Thermodynamic Cycles

• Source/Sink - Sources et puits de fluides avec proprietes thermodynamiques (CoolProp)
• Compressor - Modeles de compresseurs (isentropique, volumetrique)
• Pump - Pompes hydrauliques avec courbes caracteristiques
• Evaporator/Condenser - Evaporateurs et condenseurs pour cycles frigorifiques
• HEX - Echangeurs de chaleur (NTU, DTLM, air-cooled)
• Expansion_Valve - Detendeurs isenthalpiques
• Desuperheater - Desurchauffeurs
• Chiller - Cycles frigorifiques complets (GUI Tkinter disponible)

Hydraulique / Hydraulics

• StraightPipe - Calcul de pertes de charge en conduite droite (Darcy-Weisbach)
• TA_Valve - Vannes d''equilibrage IMI TA (120+ references, interpolation Kv)

CTA et Batiment / AHU and Building

• FreshAir - Air neuf avec calcul des proprietes psychrometriques
• HeatingCoil - Batteries de chauffage (NUT)
• Humidifier - Humidification (vapeur, adiabatique)
• HeatExchanger - Echangeurs rotatifs et a plaques
• BuildingRC - Modele thermique dynamique de batiment (methode RC)

Transferts Thermiques / Heat Transfer

• CompositeWall - Murs composites multicouches avec resistances thermiques
• PipeInsulation - Analyse d''isolation de tuyauteries

Energie et Facturation / Energy and Billing

• TURPE - Calcul du tarif d''utilisation des reseaux electriques (BT/HTA)
• CEE - Certificats d''Economies d''Energie

Donnees Meteo / Weather Data

• OpenWeatherMap - API temps reel (temperature, humidite)
• MeteoCiel - Scraping donnees historiques avec calcul DJU

Optimisation Energetique / Energy Optimization

• PinchAnalysis - Analyse Pinch pour integration energetique (GCC, courbes composites)
• IPMVP - Protocole de mesure et verification des economies d''energie
• PV - Simulation de production photovoltaique (pvlib)

Outils / Tools

• NodeEditor - Editeur graphique de flux energetiques (PyQt5)
• TkinterGUI - Interfaces graphiques pour chiller
• PyqtSimulator - Simulateur temps reel

---

Table des matieres / Table of Contents

• 1. ThermodynamicCycles Package
  • 1.1. Fluid Source
  • 1.2. Sink
  • 1.3. Compressor
  • 1.4. Water Heat Storage
  • 1.5. Chiller
  • 1.6. Echangeurs de chaleur
  • 1.7. Hydraulique
• 2. AHU Modules
  • 2.1. Fresh AHU Example
  • 2.2. Building RC Model
• 3. PinchAnalysis
• 4. Donnees meteo
  • 4.1. OpenWeatherMap
  • 4.2. MeteoCiel
• 5. IPMVP
• 6. Production solaire
• 7. Calcul du TURPE
• 8. Vannes d''equilibrage IMI TA

---

1. ThermodynamicCycles Package

Le package ThermodynamicCycles fournit des composants pour modeliser des cycles thermodynamiques complets : sources, puits, compresseurs, evaporateurs, condenseurs, vannes de detente, pompes, echangeurs de chaleur, etc.

1.1. Fluid Source

La classe Source represente une source de fluide avec des proprietes thermodynamiques definies.

1.1.1. Parametres d''entree / Input parameters

Symbol	Description	SI Units	Used Units
Ti_degC	Inlet temperature	K	degC
fluid	Fluid/Refrigerant name	String	"air", "ammonia", "R134a", "water", ...
F, 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

1.1.2. Exemple / Example

from ThermodynamicCycles.Source import Source

# Create Source Object
SOURCE = Source.Object()

# Data Input
SOURCE.Pi_bar = 1.01325
SOURCE.fluid = "air"
SOURCE.F = 1  # kg/s

# Calculate Object
SOURCE.calculate()

# Data output
print(SOURCE.df)

1.2. Sink

La classe Sink represente un puits de fluide (sortie du systeme).

1.2.1. Exemple / Example

from ThermodynamicCycles.Sink import Sink

# Create Sink object
SINK = Sink.Object()

# Input data
SINK.Inlet.fluid = "air"
SINK.Inlet.F = 0.334
SINK.Inlet.P = 101325
SINK.Inlet.h = 420000

# Calculate SINK
SINK.calculate()

# Print result
print(SINK.df)
print(f"Outlet Temperature: {SINK.To_degC} degC")

1.3. Compressor

Modele de compresseur avec rendement isentropique et refroidissement optionnel.

1.3.1. Exemple / Example

from ThermodynamicCycles.Source import Source
from ThermodynamicCycles.Compressor import Compressor
from ThermodynamicCycles.Sink import Sink
from ThermodynamicCycles.Connect import Fluid_connect

# Create Objects
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

COMP.eta_is = 0.80  # Isentropic efficiency
COMP.Tdischarge_target = 80  # Discharge temperature in degC
COMP.HP = 7.5 * 100000  # Discharge pressure in Pa
COMP.Q_comp = 48745.761  # Power in W

# 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(SOURCE.df)
print(COMP.df)
print(SINK.df)

1.4. Water Heat Storage

Modele de stockage thermique avec reservoir melange.

1.4.1. Mixed Tank

from ThermodynamicCycles import MixedStorage
from ThermodynamicCycles.Source import Source
from ThermodynamicCycles.Sink import Sink
from ThermodynamicCycles.Connect import Fluid_connect
import pandas as pd
import os

# Read Excel file
data = pd.read_excel(os.path.join(os.path.dirname(__file__), ''HotWaterStorage.xlsx''))
data[''Timestamp''] = pd.to_datetime(data[''Timestamp''], format="%d/%m/%y %H:%M:%S")
rows = data.shape[0]

# Initialize result DataFrames
df_result = pd.DataFrame()
df_source = pd.DataFrame()
df_str = pd.DataFrame()
df_sink = pd.DataFrame()

# Create Objects
SOURCE = Source.Object()
SINK = Sink.Object()
STR = MixedStorage.Object()

# Parameters
STR.V = 4  # Volume in m3
STR.Tinit_degC = 40  # Initial temperature
STR.t = 3600  # Time step in seconds

for r in range(1, rows):
    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]

    dt = (data["Timestamp"][r] - data["Timestamp"][r-1]).total_seconds()
    STR.t = dt

    SOURCE.calculate()
    Fluid_connect(STR.Inlet, SOURCE.Outlet)
    STR.calculate()
    Fluid_connect(SINK.Inlet, STR.Outlet)
    SINK.calculate()

    df_str = pd.concat([df_str, STR.df.T], ignore_index=True)
    df_source = pd.concat([df_source, SOURCE.df.T], ignore_index=True)
    df_sink = pd.concat([df_sink, SINK.df.T], ignore_index=True)

# Merge results
df_result = df_str.merge(df_sink, on=[''Timestamp'']).merge(df_source, on=[''Timestamp''])
print(df_result)

1.5. Chiller

Cycle frigorifique complet avec evaporateur, compresseur, desurchauffeur, condenseur et detendeur.

1.5.1. Interface graphique / GUI Application

from TkinterGUI import Chiller

1.5.2. Modele oriente objet / Object-Oriented Model

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 components
EVAP = Evaporator.Object()
COMP = Compressor.Object()
DESURCH = Desuperheater.Object()
COND = Condenser.Object()
DET = Expansion_Valve.Object()

# Cycle parameters
fluid = "R134a"
EVAP.fluid = fluid
EVAP.Inlet.F = 1  # kg/s
EVAP.LP_bar = 2.930154  # bar
EVAP.surchauff = 2  # superheating
EVAP.Inlet.h = CP.PropsSI(''H'', ''P'', 1*1e5, ''T'', 40+273.15, fluid)

# Compressor parameters
COMP.Tcond_degC = 40
COMP.eta_is = 0.8
COMP.Tdischarge_target = 80  # degC
COMP.Q_comp = 100000  # W

# Condenser parameters
COND.subcooling = 2  # degC

# Calculation
EVAP.calculate()
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()

# Performance
EER = EVAP.Q_evap / COMP.Q_comp
Q_condTot = COND.Q_cond + DESURCH.Qdesurch
COP = Q_condTot / COMP.Q_comp

print(f"EER = {round(EER, 1)}")
print(f"Q_condTot = {round(Q_condTot/1000, 1)} kW")
print(f"COP = {round(COP, 1)}")

1.6. Echangeurs de chaleur / Heat Exchangers

1.6.1. Air-Cooled Heat Exchanger (Aerorefrigerant)

Modele de dimensionnement d''aerorefrigerant pour refroidir des fluides de procede par air ambiant.

1.6.1.1. Caracteristiques

• Fluides supportes : eau, hydrocarbures legers, gasoil
• Coefficient U automatique : 850 W/m2.K (eau), 540 W/m2.K (hydrocarbure), 400 W/m2.K (gasoil)
• Nombre de rangs : calcule automatiquement selon deltaT (3 a 7 rangs)
• Vitesse d''air : 2.5 a 3.55 m/s selon nombre de rangs
• Dimensionnement ventilateurs : debit, diametre, puissance electrique

1.6.1.2. Exemple / Example

from ThermodynamicCycles.HEX.AirCooled_HEX import Object as AirCooled_HEX
import math

# Creer l''aerorefrigerant / Create air-cooled heat exchanger
AERO = AirCooled_HEX()

# Parametres du fluide process / Process fluid parameters
AERO.fluid = "water"
AERO.Ti_fluid = 165  # Eau chaude a 165°C / Hot water at 165°C
AERO.To_fluid = 80   # Refroidir a 80°C / Cool to 80°C
AERO.F_fluid = 10    # 10 kg/s
AERO.Cp_fluid = 4200  # J/kg.K (eau / water)

# Parametres de l''air / Air parameters
AERO.Ti_air = 35     # Air ambiant a 35°C / Ambient air at 35°C
AERO.TminAmb = 10    # Temperature minimale de design / Design minimum temperature
AERO.Cp_air = 1005   # J/kg.K
AERO.rho_air = 1.15  # kg/m3

# Geometrie / Geometry
AERO.largeur_baie = 6  # Largeur de baie 6 m / Bay width 6 m
AERO.L_tube = 12       # Longueur de tubes 12 m / Tube length 12 m
AERO.nb_faisceaux = 2  # 2 bundles

# Calcul de la puissance thermique / Calculate thermal duty
AERO.Qth_fluid = AERO.F_fluid * AERO.Cp_fluid * (AERO.Ti_fluid - AERO.To_fluid)

# Coefficient U pour l''eau / U coefficient for water
AERO.U = 850  # W/m2.K

# Determination du nombre de rangs / Determine number of rows
deltaT = AERO.Ti_fluid - AERO.Ti_air
if deltaT <= 10:
    AERO.nb_rangs = 3
elif deltaT <= 50:
    AERO.nb_rangs = 4
elif deltaT <= 90:
    AERO.nb_rangs = 6
else:
    AERO.nb_rangs = 7

# Voir test_AirCooled_HEX.py pour le calcul complet
# See test_AirCooled_HEX.py for complete calculation
print(f"Puissance thermique / Thermal duty: {AERO.Qth_fluid/1000:.1f} kW")
print(f"Nombre de rangs / Number of rows: {AERO.nb_rangs}")

1.7. Hydraulique

1.7.1. Straight Pipe (Darcy-Weisbach)

from ThermodynamicCycles.Hydraulic import StraightPipe
from ThermodynamicCycles.Source import Source
from ThermodynamicCycles.Sink import Sink
from ThermodynamicCycles.Connect import Fluid_connect

# Create objects
SOURCE = Source.Object()
PIPE1 = StraightPipe.Object()
PIPE2 = StraightPipe.Object()
SINK = Sink.Object()

# Source configuration
SOURCE.fluid = "water"
SOURCE.Ti_degC = 25
SOURCE.Pi_bar = 1
SOURCE.F_m3h = 100
SOURCE.calculate()

# Pipe 1 configuration
PIPE1.d_hyd = 0.050  # m (DN50)
PIPE1.L = 100  # m
PIPE1.K = 0.00002  # m (roughness)

# Pipe 2 configuration
PIPE2.d_hyd = 0.2  # m (DN200)
PIPE2.L = 100  # m
PIPE2.K = 0.00002  # m

# Connect and calculate
Fluid_connect(PIPE1.Inlet, SOURCE.Outlet)
PIPE1.calculate()
Fluid_connect(PIPE2.Inlet, PIPE1.Outlet)
PIPE2.calculate()
Fluid_connect(SINK.Inlet, PIPE2.Outlet)
SINK.calculate()

# Results
print(SOURCE.df)
print(PIPE1.df)
print(PIPE2.df)
print(SINK.df)

---

2. AHU Modules

Modules pour centrales de traitement d''air (CTA).

2.1. Fresh AHU Example

Modele CTA avec air neuf, batterie de chauffage et humidificateur.

from AHU import FreshAir
from AHU import HeatingCoil
from AHU.Humidification import Humidifier
from AHU.Connect import Air_connect

# Create Objects
AN = FreshAir.Object()
BC = HeatingCoil.Object()
HMD = Humidifier.Object()

# Fresh Air Input
AN.F_m3h = 3000  # m3/h
AN.T = 14  # degC
AN.RH_FreshAir = 71  # %

# Heating Coil Target
BC.To_target = 15  # degC

# Humidifier Target
HMD.wo_target = 8  # g/kg dry air
HMD.HumidType = "vapeur"  # or "adiabatique"

# Calculate
AN.calculate()
Air_connect(BC.Inlet, AN.Outlet)
BC.calculate()
Air_connect(HMD.Inlet, BC.Outlet)
HMD.calculate()

# Results
print(f"Fresh Air Absolute Humidity: {round(AN.w, 1)} g/kg")
print(f"Fresh Air Wet-Bulb Temperature: {round(AN.T_hum, 1)} degC")
print(f"Fresh Air Specific Enthalpy: {round(AN.h, 3)} kJ/kg")
print(f"Heating Coil Thermal Power: {round(BC.Qth, 1)} kW")
print(f"Heating Coil Relative Humidity: {round(BC.RH_out, 1)} %")
print(f"Humidifier Steam mass flow rate: {round(HMD.F_water, 3)} kg/s")

2.2. Building RC Model

Modele thermique dynamique de batiment base sur la methode RC (resistance-capacite).

from AHU.Building.BuildingRC import Object as Building
import matplotlib.pyplot as plt
import numpy as np

# Building configuration
building = Building()

# Parameters
building.e_mur = 0.4  # Wall thickness [m]
building.lambda_mur = 1.2  # Thermal conductivity [W/m.K]
building.rho_mur = 2100  # Density [kg/m3]
building.cp_mur = 1000  # Specific heat [J/kg.K]
building.V_int = 2000.0  # Interior volume [m3]
building.A_env = 1000.0  # Envelope area [m2]
building.A_sol = 400.0  # Floor area [m2]
building.h_ext = 20.0  # External convection [W/m2.K]
building.h_int = 5.0  # Internal convection [W/m2.K]

# Temperature setpoints
building.T_consigne_chaud = 19.0  # Heating setpoint [degC]
building.T_consigne_froid = 25.0  # Cooling setpoint [degC]

# Simulation
hours = 24 * 5  # 5 days
dt = 60  # Time step [s]
building.T_int = 22  # Initial indoor [degC]
building.T_mur = 19  # Initial wall [degC]

# External temperature
T_moy = 25  # Average [degC]
Delta_T = 10  # Amplitude [degC]
time_sim = np.arange(0, hours * 3600, dt) / 3600  # [h]
T_ext = T_moy + Delta_T * np.sin(2 * np.pi * (time_sim - 6) / 48)

# Air renewal
taux_renouvellement = 0.5  # volumes/hour
building.debit_soufflage = building.V_int * taux_renouvellement

# Simulation loop
for t, T_ext_t in zip(range(0, hours * 3600, dt), T_ext):
    building.T_ext = T_ext_t
    building.update(dt)

# Results
history = building.history
time = np.arange(len(history[''T_int''])) * dt / 3600

plt.figure(figsize=(14, 10))
plt.subplot(3, 1, 1)
plt.plot(time, history[''T_int''], label=''Indoor Temperature'', color=''blue'')
plt.plot(time, history[''T_ext''], label=''Outdoor Temperature'', color=''red'', linestyle=''--'')
plt.axhline(building.T_consigne_chaud, color=''cyan'', linestyle='':'', label=''Heating Setpoint'')
plt.axhline(building.T_consigne_froid, color=''orange'', linestyle='':'', label=''Cooling Setpoint'')
plt.xlabel(''Time (h)'')
plt.ylabel(''Temperature (degC)'')
plt.legend()
plt.grid(True)

plt.subplot(3, 1, 2)
plt.plot(time, history[''P_chaud''], label=''Heating Power'', color=''red'')
plt.plot(time, history[''P_froid''], label=''Cooling Power'', color=''blue'')
plt.xlabel(''Time (h)'')
plt.ylabel(''Power (W)'')
plt.legend()
plt.grid(True)

plt.tight_layout()
plt.show()

---

3. PinchAnalysis

Analyse Pinch pour l''integration energetique des procedes industriels.

from PinchAnalysis.PinchCalculation import PinchCalculation
import pandas as pd
import matplotlib.pyplot as plt

# Input DataFrame
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 results
print(f"Temperature: {T}")
print(f"Cold utility: {utilite_froide} kW")
print(f"Hot utility: {utilite_chaude} kW")

# Plot Composite Curves
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))

ax1.plot(plot_ccf[:, 0], T, color=''tab:blue'', label=''Cold Composite Curve'')
ax1.plot(plot_ccc[:, 0], T, color=''tab:red'', label=''Hot Composite Curve'')
ax1.set_xlabel(''Power (kW)'')
ax1.set_ylabel(''Temperature (degC)'')
ax1.legend()
ax1.grid(True)
ax1.set_title(''Composite Curves'')

ax2.plot(plot_GCC[:, 0], T, color=''tab:orange'', linewidth=2)
ax2.set_xlabel(''Power (kW)'')
ax2.set_ylabel(''Temperature (degC)'')
ax2.grid(True)
ax2.set_title(''Grand Composite Curve'')

plt.tight_layout()
plt.show()

---

4. Donnees meteo

4.1. OpenWeatherMap

Recuperation de donnees meteo en temps reel via API OpenWeatherMap.

from OpenWeatherMap import OpenWeatherMap_call_location

# Get temperature and humidity from GPS coordinates
df = OpenWeatherMap_call_location.API_call_location("48.862725", "2.287592")

print(df)

Resultat :

                   Timestamp  T(degC)  RH(%)
0 2023-03-09 14:09:01.941527  14.39     72

4.2. MeteoCiel

Scraping de donnees meteo historiques depuis les stations MeteoCiel avec calcul automatique des DJU.

from datetime import datetime
from MeteoCiel.MeteoCiel_Scraping import MeteoCiel_histoScraping

# Input data
code2 = 10637  # Weather station code
date_debut = datetime(2023, 1, 1)
date_fin = datetime.now()

# Scraping function
df_histo, df_day, df_month, df_year = MeteoCiel_histoScraping(
    code2, 
    date_debut, 
    date_fin,
    base_chauffage=18,
    base_refroidissement=23
)

# Save to Excel
df_histo.to_excel(f"Meteociel_station_{date_debut.date()}_to_{date_fin.date()}.xlsx")
df_day.to_excel(f"day_Meteociel_station_{date_debut.date()}_to_{date_fin.date()}.xlsx")
df_month.to_excel(f"month_Meteociel_station_{date_debut.date()}_to_{date_fin.date()}.xlsx")
df_year.to_excel(f"year_Meteociel_station_{date_debut.date()}_to_{date_fin.date()}.xlsx")

---

5. IPMVP

Protocole international de mesure et verification de la performance (IPMVP) pour quantifier les economies d''energie.

from IPMVP.IPMVP import Mathematical_Models
import os
from datetime import datetime
import pandas as pd

# Data file
filename = "Input-Data.xlsx"
date_column = "Timestamp"

# Read data
directory = os.getcwd()
file_directory = os.path.join(directory, filename)
df = pd.read_excel(file_directory)

# Convert date to datetime index
df[date_column] = pd.to_datetime(df[date_column])
df = df.set_index(df[date_column])

# Define periods
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)

# Daily IPMVP Model
df_daily = df.resample(''D'').sum()
X = df_daily[["x1", "x2", "x3", "x4", "x5", "x6"]]
y = df_daily["y"]

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
)

# Weekly IPMVP Model
weekly_X = X.resample(''W'').sum()
weekly_y = y.resample(''W'').sum()
week_model = Mathematical_Models(
    weekly_y, weekly_X,
    start_baseline_period, end_baseline_period,
    start_reporting_period, end_reporting_period
)

# Monthly IPMVP Model
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
)

---

6. Production solaire

Simulation de production photovoltaique avec pvlib.

from PV.ProductionElectriquePV import SolarSystem

# Create solar system
system = SolarSystem(
    latitude=48.8566,
    longitude=2.3522,
    location_name=''Paris'',
    tilt=34,
    timezone=''Etc/GMT-1'',
    azimuth=180.0,
    system_capacity=48.9  # kWp
)

# Retrieve data
system.retrieve_module_inverter_data()
system.retrieve_weather_data()

# Calculate
system.calculate_solar_parameters()

# Plot
system.plot_annual_energy()

---

7. Calcul du TURPE

Calcul du Tarif d''Utilisation des Reseaux Publics d''Electricite (TURPE) pour les differents domaines de tension.

from Facture.TURPE import input_Contrat, TurpeCalculator, input_Facture, input_Tarif

# Invoice data
facture = input_Facture(
    start="2022-09-01",
    end="2022-09-30",
    heures_depassement=0,
    depassement_PS_HPB=64,
    kWh_pointe=0,
    kWh_HPH=0,
    kWh_HCH=0,
    kWh_HPB=26635,
    kWh_HCB=12846
)

# Contract data
contrat = input_Contrat(
    domaine_tension="BT > 36 kVA",
    PS_pointe=129,
    PS_HPH=129,
    PS_HCH=129,
    PS_HPB=129,
    PS_HCB=250,
    version_utilisation="LU",
    pourcentage_ENR=100
)

# Tariff data
tarif = input_Tarif(
    c_euro_kWh_pointe=0.2,
    c_euro_kWh_HPB=0.15,
    c_euro_kWh_HCB=0.12,
    c_euro_kWh_HPH=0.18,
    c_euro_kWh_HCH=0.16,
    c_euro_kwh_CSPE_TICFE=0.05,
    c_euro_kWh_certif_capacite=0.03,
    c_euro_kWh_ENR=0.1,
    c_euro_kWh_ARENH=0.09
)

# Create calculator
turpe_calculator = TurpeCalculator(contrat, tarif, facture)

# Calculate
turpe_calculator.calculate_turpe()

# Results
print(f"Grid usage cost (euro): {turpe_calculator.euro_TURPE:.2f}")
print(f"Taxes and contributions (euro): {turpe_calculator.euro_taxes_contrib:.2f}")

---

8. Vannes d''equilibrage IMI TA

Calcul de pertes de charge dans les vannes d''equilibrage hydraulique IMI TA avec interpolation lineaire automatique du coefficient Kv.

8.1. Caracteristiques

• 120+ references : STAD, STAV, TBV, TBV-C, STAF, STAF-SG, STAF-R, STAG, STA, MDFO, STAP, STAM, STAZ, STAP-R
• Interpolation lineaire : Calcul du Kv pour n''importe quelle position intermediaire (ex: 4.5 tours)
• DN 10 a DN 900 : Large gamme de diametres nominaux
• Donnees officielles : Valeurs Kv issues des documentations IMI TA

8.2. Exemple d''utilisation

from ThermodynamicCycles.Hydraulic import TA_Valve
from ThermodynamicCycles.Source import Source
from ThermodynamicCycles.Connect import Fluid_connect

# Create objects
SOURCE = Source.Object()
vanne = TA_Valve.Object()

# Source configuration
SOURCE.Ti_degC = 25
SOURCE.Pi_bar = 3.0
SOURCE.fluid = "Water"
SOURCE.F_m3h = 70
SOURCE.calculate()

# Valve configuration
vanne.dn = "STAF-DN100"
vanne.nb_tours = 4.5

# Connect and calculate
Fluid_connect(vanne.Inlet, SOURCE.Outlet)
vanne.calculate()

# Display results
print(vanne.df)
print(f"Inlet pressure: {vanne.Inlet.P/100000:.3f} bar")
print(f"Outlet pressure: {vanne.Outlet.P/100000:.3f} bar")
print(f"Pressure drop: {vanne.delta_P/100000:.3f} bar")
print(f"Flow rate: {SOURCE.F_m3h:.2f} m3/h")

8.3. Familles de vannes disponibles

Famille	Description	Plage DN
STAD	Vanne d''equilibrage manuelle filetee PN 25	DN 10-50
STAV	Vanne d''equilibrage Venturi filetee PN 20	DN 15-50
TBV	Vanne terminale d''equilibrage manuelle	DN 15-20
TBV-C	Vanne terminale + controle TA-Scope	DN 10-20
STAF	Vanne a brides fonte PN 16/25	DN 20-400
STAF-SG	Variante STAF fonte GS PN 16/25	DN 65-400
STAF-R	Vanne version retour PN 16/25	DN 65-200
STAG	Vanne grooved Victaulic PN 16	DN 65-300
STA	Ancienne vanne TA (archive)	DN 15-150
MDFO	Orifice fixe de mesure (Kv fixe)	DN 20-900
STAP	Regulateur deltaP differentielle	DN 15-100
STAM	Regulateur deltaP boucles/colonnes	DN 15-50
STAZ/STAP-R	Regulateurs deltaP legacy	DN 15-50

---

Dependances / Dependencies

Le package installe automatiquement les dependances suivantes :

• pandas - Manipulation de donnees
• numpy - Calculs numeriques
• matplotlib - Visualisation
• CoolProp (via pyfluids) - Proprietes thermodynamiques
• pvlib - Simulation photovoltaique
• scikit-learn - Machine learning (IPMVP)
• statsmodels - Modeles statistiques
• beautifulsoup4 - Web scraping (MeteoCiel)
• PyQt5 - Interfaces graphiques
• tkintertable - Tables Tkinter
• gekko - Optimisation

License

MIT License - Zoheir HADID

Contribution

Les contributions sont les bienvenues ! N''hesitez pas a ouvrir une issue ou une pull request sur GitHub.

Contact

Auteur : Zoheir HADID
Email : [e-mail]

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Version : 20251118006
Python : >= 3.7