Modified heat pump import to compute coefficient of performance data

imports/energy_systems/xlsx_heat_pump_parameters.py

@@ -7,9 +7,12 @@ Contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
 
 import pandas as pd
 from typing import Dict
-from typing import List
 from city_model_structure.energy_systems.heat_pump import HeatPump
 from city_model_structure.energy_system import EnergySystem
+from scipy.optimize import curve_fit
+import numpy as np
+from typing import List
+import itertools
 
 
 class XlsxHeatPumpParameters:
@@ -71,16 +74,19 @@ class XlsxHeatPumpParameters:
       c_data = self._extract_heat_pump_data(v_cool)
       heat_pump.cooling_capacity = c_data[0]
       heat_pump.cooling_comp_power = c_data[1]
-      heat_pump.cooling_water_flow = c_data[2]
+      heat_pump.cooling_capacity_coff = self._compute_coefficients(c_data[0], "cool")
+      heat_pump.cooling_comp_power_coff = self._compute_coefficients(c_data[1], "cool")
       heat_pump.heating_capacity = h_data[0]
       heat_pump.heating_comp_power = h_data[1]
-      heat_pump.heating_water_flow = h_data[2]
+      heat_pump.heating_capacity_coff = self._compute_coefficients(h_data[0])
+      heat_pump.heating_comp_power_coff = self._compute_coefficients(h_data[1])
+
       energy_system = EnergySystem('{} capacity heat pump'.format(heat_pump.model), 0, [], None)
       energy_system.heat_pump = heat_pump
       self._city.add_city_object(energy_system)
     return self._city
 
-  def _extract_heat_pump_data(self, heat_pump_capacity_data) -> [List, List, List]:
+  def _extract_heat_pump_data(self, heat_pump_capacity_data) -> [List, List]:
     """
     Fetches a list of metric based data for heat pump for various temperature,
     eg. cooling capacity data for 12 capacity heat pump
@@ -91,9 +97,42 @@ class XlsxHeatPumpParameters:
     """
     cooling_heating_capacity_data = []
     compressor_power_data = []
-    water_flow_data = []
     for _, metric_data in heat_pump_capacity_data.items():
       cooling_heating_capacity_data.append(metric_data[0])
       compressor_power_data.append(metric_data[1])
-      water_flow_data.append(metric_data[2])
+    return [cooling_heating_capacity_data, compressor_power_data]
-    return [cooling_heating_capacity_data, compressor_power_data, water_flow_data]
+
+  def _compute_coefficients(self, heat_pump_data, data_type="heat") -> List[float]:
+    """
+    Compute heat output and electrical demand coefficients
+    from heating and cooling performance data
+    :param heat_pump_data: a list of heat pump data. eg. cooling capacity
+    :param data_type: string to indicate if data is cooling performance data
+    or heating performance data
+    :return: Tuple[Dict, Dict]
+    """
+    out_temp = [25, 30, 32, 35, 40, 45] * 6
+    heat_x_values = np.repeat([-5, 0, 7, 10, 15], 6)
+    cool_x_values = np.repeat([6, 7, 8, 9, 10, 11], 6)
+    x_values = heat_x_values if data_type == "heat" else cool_x_values
+    # convert list of lists to one list
+    heat_pump_data = list(itertools.chain.from_iterable(heat_pump_data))
+
+    # Compute heat output coefficients
+    popt, _ = curve_fit(self._objective_function, [x_values, out_temp], heat_pump_data)
+    return popt.tolist()
+
+  def _objective_function(self, xdata, a1, a2, a3, a4, a5, a6):
+    """
+    Objective function for computing coefficients
+    :param xdata:
+    :param a1: float
+    :param a2: float
+    :param a3: float
+    :param a4: float
+    :param a5: float
+    :param a6: float
+    :return:
+    """
+    x, y = xdata
+    return (a1 * x ** 2) + (a2 * x) + (a3 * x * y) + (a4 * y) + (a5 * y ** 2) + a6