86 lines
3.5 KiB
Python
86 lines
3.5 KiB
Python
"""
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HeatPumpExport exports heatpump coefficient into several formats
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SPDX - License - Identifier: LGPL - 3.0 - or -later
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Copyright © 2021 Project Author Peter Yefi peteryefi@gmail.com
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"""
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import numpy as np
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from scipy.optimize import curve_fit
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from typing import Dict, Tuple
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import pandas as pd
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class HeatPumpExport:
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"""
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Exports heat pump values as coefficients
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of some defined function
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"""
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def __init__(self, base_path, city):
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self._base_path = (base_path / 'heat_pumps/coefficients.xlsx')
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self._city = city
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def export_xlsx(self):
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"""
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Writes the coefficients computed from heat performance
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and cooling performance data to excel sheet
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:return: None
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"""
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writer = pd.ExcelWriter(self._base_path)
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heat_column_names = ["a1", "a2", "a3", "a4", "a5", "a6"]
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cool_column_names = ["b1", "b2", "b3", "b4", "b5", "b6"]
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heat_coff, cool_coff = self._compute_coefficients()
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for (k_cool, v_cool), (k_heat, v_heat) in \
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zip(heat_coff.items(), cool_coff.items()):
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heat_df = pd.DataFrame([v_heat["heat_cap"], v_heat["comp_power"]], columns=heat_column_names,
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index=["Heat Capacity", "Compressor Power"])
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heat_df.to_excel(writer, sheet_name=k_heat)
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cool_df = pd.DataFrame([v_heat["cool_cap"], v_heat["comp_power"]], columns=cool_column_names,
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index=["Cooling Capacity", "Compressor Power"])
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cool_df.to_excel(writer, sheet_name=k_cool, startrow=10)
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writer.save()
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def _compute_coefficients(self) -> Tuple[Dict, Dict]:
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"""
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Compute heat output and electrical demand coefficients
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from heating and cooling performance data
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:return: Tuple[Dict, Dict]
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"""
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out_temp = [25, 30, 32, 35, 40, 45] * 6
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heat_x_values = np.repeat([-5, 0, 7, 10, 15], 6)
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cool_x_values = np.repeat([6, 7, 8, 9, 10, 11], 6)
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cooling_coff = {}
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heating_coff = {}
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for energy_system in self._city.energy_systems:
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# Compute heat output coefficients
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heating_cap_popt, _ = curve_fit(self._objective_function, [heat_x_values, out_temp],
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energy_system.heat_pump.heating_capacity)
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heating_comp_power_popt, _ = curve_fit(self._objective_function, [heat_x_values, out_temp],
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energy_system.heat_pump.heating_comp_power)
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# Compute electricity demand coefficients
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cooling_cap_popt, _ = curve_fit(self._objective_function, [cool_x_values, out_temp],
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energy_system.heat_pump.cooling_capacity)
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cooling_comp_power_popt, _ = curve_fit(self._objective_function, [cool_x_values, out_temp],
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energy_system.heat_pump.cooling_comp_power)
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heating_coff[energy_system.heat_pump.model] = {"heat_cap": heating_cap_popt.tolist(),
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"comp_power": heating_comp_power_popt.tolist()}
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cooling_coff[energy_system.heat_pump.model] = {"cool_cap": cooling_cap_popt.tolist(),
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"comp_power": cooling_comp_power_popt.tolist()}
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return heating_coff, cooling_coff
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def _objective_function(self, xdata, a1, a2, a3, a4, a5, a6):
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"""
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Objective function for computing coefficients
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:param xdata:
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:param a1: float
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:param a2: float
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:param a3: float
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:param a4: float
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:param a5: float
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:param a6: float
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:return:
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"""
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x, y = xdata
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return (a1 * x ** 2) + (a2 * x) + (a3 * x * y) + (a4 * y) + (a5 * y ** 2) + a6
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