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HP_Model.py
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#HP_Model
"""
Interpolated Power Calculator
Interpolated Calculator
This class processes data from a CSV file containing power-related information. It performs the following steps:
1. Data Preparation:
- Reads the CSV file into a pandas DataFrame.
- Sorts the DataFrame by 'PowerPercentage', 'sourceTemp', and 'condensationTemperature'.
- Converts relevant columns into a numpy 3D array, with (PowerPercentage, sourceTemp, condensationTemp) as coordinates.
- Assigns two values to each point in the array: 'heatingPower' and 'electricalPower'.
- Sorts the DataFrame by 'T_source_in', 'T_sink_out', and 'Q_dot_H'.
- Converts relevant columns into a numpy 3D array, with (T_source_in, T_sink_out, Q_dot_H) as coordinates.
- Assigns two values to each point in the array: 'n_comp_perc' and 'P_comp'.
2. KD-Tree Creation:
- Constructs a KD-Tree for the entire 3D array.
- Saves the KD-Tree within the class.
3. User Queries:
- Users can request power information for a specific point (given 'powerPercentage', 'sourceTemp', and 'condensationTemp').
- Choose either 'heatingPower' or 'electricalPower'.
- Users can request information for a specific point (given 'T_source_in', 'T_sink_out', and 'Q_dot_H').
- Choose either 'n_comp_perc', 'P_comp', 'COP' or 'eta_II'.
- If the requested point does not exist in the 3D array, the KD-Tree identifies the closest points.
- Interpolates values from these nearby points to estimate the requested power value.
- Interpolates values from these nearby points to estimate the requested value.
4. Interpolation Method:
- Utilizes a normalized inverse distance weighting (IDW) technique.
"""
import enum
import pandas as pd
from scipy.spatial import cKDTree
class PowerTypes(enum.Enum):
N_COMP_PERC = enum.auto()
ELECTRICAL = enum.auto()
class HeatPump:
# ====================================================================================================
def __init__(self, csvFile: str, mul100: bool = False, verbose: bool = False) -> None:
def __init__(self, csvFile: str, mul1000: bool = False, verbose: bool = False) -> None:
"""
Initializes the class by reading a CSV file and creating a KD-Tree.
Args:
csvFile (str): Path to the CSV file containing the data points. The CSV file should have the following headers (in order):
- PowerPercentage
- SourceTemp
- CondensationTemp
- HeatingPower
- ElectricalPower
mul100 (bool, optional): When True, the PowerPercentage values are multiplied by 100. Defaults to False.
- n_comp_perc
- T_source_in
- T_sink_out
- Q_dot_H
- P_comp
mul1000 (bool, optional): When True, the Q_dot_H values are converted from kW to W. Defaults to False.
verbose (bool, optional): If True, feedback messages are printed during initialization. Defaults to False.
Returns:
None
"""
self.__readCSV(csvFile, mul100, verbose)
self.__readCSV(csvFile, mul1000, verbose)
self.__createKDTree(verbose)
# ====================================================================================================
def __readCSV(self, csvFile: str, mul100: bool = False, verbose: bool = False) -> None:
def __readCSV(self, csvFile: str, mul1000: bool = False, verbose: bool = False) -> None:
"""
Reads and sorts the CSV file into a pandas DataFrame. The DataFrame is then extracted into NumPy n-dimensional arrays.
Args:
csvFile (str): Path to the CSV file containing the data points. The CSV file should have the following headers (in order):
- PowerPercentage
- SourceTemp
- CondensationTemp
- HeatingPower
- ElectricalPower
mul100 (bool, optional): When True, the PowerPercentage values are multiplied by 100. Defaults to False.
- n_comp_perc
- T_source_in
- T_sink_out
- Q_dot_H
- P_comp
mul1000 (bool, optional): When True, the Q_dot_H values are converted from kW to W. Defaults to False.
verbose (bool, optional): If True, feedback messages are printed during initialization. Defaults to False.
Returns:
......@@ -78,15 +83,15 @@ def __readCSV(self, csvFile: str, mul100: bool = False, verbose: bool = False) -
self.dataframe = pd.read_csv(csvFile, sep=';')
# Sort the DataFrame by specified columns and reset the index
self.dataframe = self.dataframe.sort_values(by=['PowerPercentage', 'SourceTemp', 'CondensationTemp']).reset_index(drop=True)
self.dataframe = self.dataframe.sort_values(by=['T_source_in', 'T_sink_out', 'Q_dot_H']).reset_index(drop=True)
if mul100:
self.dataframe['PowerPercentage'] *= 100
if mul1000:
self.dataframe['Q_dot_H'] *= 1000
# Extract relevant columns
self.coords = self.dataframe[['PowerPercentage', 'SourceTemp', 'CondensationTemp']].values
self.heatingPower = self.dataframe["HeatingPower"].values
self.electricalPower = self.dataframe["ElectricalPower"].values
self.coords = self.dataframe[['T_source_in', 'T_sink_out', 'Q_dot_H']].values
self.powerPercentage = self.dataframe["n_comp_perc"].values
self.electricalPower = self.dataframe["P_comp"].values
if verbose:
print(f"The CSV file '{csvFile}' has been read.")
......@@ -113,26 +118,26 @@ def __createKDTree(self, verbose: bool = False) -> None:
# ====================================================================================================
def __getPower(self, powerPercentage: float, sourceTemp: float, condensationTemp: float, K: int = 3, type: str = "heating") -> float:
def __getPowerValue(self, T_source_in: float, T_sink_out: float, Q_dot_H: float, K: int = 3, type: PowerTypes = PowerTypes.ELECTRICAL) -> float:
"""
Calculates the interpolated power value at the specified coordinate point.
Calculates the interpolated power or power related value at the specified coordinate point.
Args:
powerPercentage (float): Power percentage value.
sourceTemp (float): Source temperature.
condensationTemp (float): Condensation temperature.
T_source_in (float): Source temerature (in).
T_sink_out (float): Sink temperature (out).
Q_dot_H (float): Heating power.
K (int, optional): Number of neighbors to consider. Defaults to 3.
type (str, optional): Type of power ('heating' or 'electrical'). Defaults to 'heating'.
type (PowerTypes, optional): Type of power value. Defaults to ELECTRICAL.
Returns:
float: Interpolated power value.
"""
if type not in ["heating", "electrical"]:
print(f"Invalid type '{type}' selected. Please choose 'heating' or 'electrical'.")
if type not in PowerTypes:
print("Invalid type selected.")
return 0
point = (powerPercentage, sourceTemp, condensationTemp)
point = (T_source_in, T_sink_out, Q_dot_H)
distances, indices = self.kdTree.query(point, k=K)
totalDistance = sum(distances)
......@@ -145,46 +150,85 @@ def __getPower(self, powerPercentage: float, sourceTemp: float, condensationTemp
weights = [(totalDistance - d) / (totalDistance * (K - 1)) for d in distances]
# Interpolate values
interpolatedPower = 0
interpolatedPowerValue = 0
for index in range(K):
nearbyPower = self.heatingPower[indices[index]] if type == "heating" else self.electricalPower[indices[index]]
interpolatedPower += weights[index] * nearbyPower # Assuming each point has a 'value' attribute
nearbyPowerValue = self.powerPercentage[indices[index]] if type == PowerTypes.N_COMP_PERC else self.electricalPower[indices[index]]
interpolatedPowerValue += weights[index] * nearbyPowerValue # Assuming each point has a 'value' attribute
return interpolatedPower
return interpolatedPowerValue
# ====================================================================================================
# ====================================================================================================
def getHeatingPower(self, powerPercentage: float, sourceTemp: float, condensationTemp: float, K: int = 3) -> float:
def getN_comp_perc(self, T_source_in: float, T_sink_out: float, Q_dot_H: float, K: int = 3) -> float:
"""
Calculates the interpolated heating power value at the specified coordinate point.
Calculates the interpolated compressor rpm-percentage at the specified coordinate point.
Args:
powerPercentage (float): Power percentage value.
sourceTemp (float): Source temperature.
condensationTemp (float): Condensation temperature.
T_source_in (float): Source temerature (in).
T_sink_out (float): Sink temperature (out).
Q_dot_H (float): Heating power.
K (int, optional): Number of neighbors to consider. Defaults to 3.
Returns:
float: Interpolated heating power value.
float: Interpolated compressor rpm-percentage value.
"""
return self.__getPower(powerPercentage, sourceTemp, condensationTemp, K, "heating")
return self.__getPowerValue(T_source_in, T_sink_out, Q_dot_H, K, PowerTypes.N_COMP_PERC)
# ====================================================================================================
def getElectricalPower(self, powerPercentage: float, sourceTemp: float, condensationTemp: float, K: int = 3) -> float:
def getElectricalPower(self, T_source_in: float, T_sink_out: float, Q_dot_H: float, K: int = 3) -> float:
"""
Calculates the interpolated electrical power value at the specified coordinate point.
Args:
powerPercentage (float): Power percentage value.
sourceTemp (float): Source temperature.
condensationTemp (float): Condensation temperature.
T_source_in (float): Source temerature (in).
T_sink_out (float): Sink temperature (out).
Q_dot_H (float): Heating power.
K (int, optional): Number of neighbors to consider. Defaults to 3.
Returns:
float: Interpolated electrical power value.
"""
return self.__getPower(powerPercentage, sourceTemp, condensationTemp, K, "electrical")
return self.__getPowerValue(T_source_in, T_sink_out, Q_dot_H, K)
# ====================================================================================================
def getCOP(self, T_source_in: float, T_sink_out: float, Q_dot_H: float, K: int = 3) -> float:
"""
Calculates the interpolated COP value at the specified coordinate point.
Args:
T_source_in (float): Source temerature (in).
T_sink_out (float): Sink temperature (out).
Q_dot_H (float): Heating power.
K (int, optional): Number of neighbors to consider. Defaults to 3.
Returns:
float: Interpolated COP value.
"""
return (Q_dot_H / self.getElectricalPower(T_source_in, T_sink_out, Q_dot_H, K))
# ====================================================================================================
def getEta_II(self, T_source_in: float, T_sink_out: float, Q_dot_H: float, K: int = 3) -> float:
"""
Calculates the interpolated second law efficiency value at the specified coordinate point.
Args:
T_source_in (float): Source temerature (in).
T_sink_out (float): Sink temperature (out).
Q_dot_H (float): Heating power.
K (int, optional): Number of neighbors to consider. Defaults to 3.
Returns:
float: Interpolated second law efficiency value.
"""
COP = self.getCOP(T_source_in, T_sink_out, Q_dot_H, K)
COP_Carnot = (T_sink_out + 273.15) / (T_sink_out - T_source_in) # in Kelvin!
return (COP / COP_Carnot)
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