148 lines
5.5 KiB
Python
148 lines
5.5 KiB
Python
"""
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AirSourceHeatPumpParameters import the heat pump information
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SPDX - License - Identifier: LGPL - 3.0 - or -later
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Copyright © 2022 Concordia CERC group
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Project Coder Peter Yefi peteryefi@gmail.comCode contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
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"""
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import pandas as pd
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from typing import Dict
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from city_model_structure.energy_systems.air_source_hp import AirSourceHP
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from city_model_structure.energy_system import EnergySystem
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from scipy.optimize import curve_fit
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import numpy as np
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from typing import List
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import itertools
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class AirSourceHeatPumpParameters:
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"""
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AirSourceHeatPumpParameters class
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"""
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def __init__(self, city, base_path):
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self._city = city
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self._base_path = (base_path / 'heat_pumps/air_source.xlsx')
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def _read_file(self) -> Dict:
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"""
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reads xlsx file containing the heat pump information
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into a dictionary
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:return : Dict
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"""
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xl_file = pd.ExcelFile(self._base_path)
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heat_pump_dfs = {sheet_name: xl_file.parse(sheet_name)
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for sheet_name in xl_file.sheet_names}
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cooling_data = {}
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heating_data = {}
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for sheet, dataframe in heat_pump_dfs.items():
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if 'Summary' in sheet:
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continue
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# Remove nan rows and columns and extract cooling and heating data
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# for each sheet
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df = heat_pump_dfs[sheet].dropna(axis=1, how='all')
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cooling_df = df.iloc[4:34, 0:8]
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heating_df = df.iloc[4:29, 8:20]
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# extract the data into dictionaries each sheet is a key entry in the
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# dictionary
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cooling_data[sheet] = {}
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heating_data[sheet] = {}
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i = 0
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# for each sheet extract data for twout/Ta.RU temperatures. Thus, the twout
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# temp is the key for the values of pf,pa,qw data
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while i < 25:
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cooling_data[sheet][cooling_df.iloc[i][0]] = cooling_df.iloc[i + 1:i + 4, 2:8].values.tolist()
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heating_data[sheet][heating_df.iloc[i][0]] = heating_df.iloc[i + 1:i + 4, 2:8].values.tolist()
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i = i + 5
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# extract the last cooling data
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cooling_data[sheet][cooling_df.iloc[i][0]] = cooling_df.iloc[i + 1:i + 4, 2:8].values.tolist()
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return {"cooling": cooling_data, "heating": heating_data}
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def enrich_city(self):
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"""
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Enriches the city with information from file
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"""
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heat_pump_data = self._read_file()
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for (k_cool, v_cool), (k_heat, v_heat) in \
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zip(heat_pump_data["cooling"].items(), heat_pump_data["heating"].items()):
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heat_pump = AirSourceHP()
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heat_pump.model = k_cool
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h_data = self._extract_heat_pump_data(v_heat)
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c_data = self._extract_heat_pump_data(v_cool)
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heat_pump.cooling_capacity = c_data[0]
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heat_pump.cooling_comp_power = c_data[1]
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heat_pump.cooling_capacity_coff = self._compute_coefficients(c_data, "cool")
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heat_pump.heating_capacity = h_data[0]
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heat_pump.heating_comp_power = h_data[1]
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heat_pump.heating_capacity_coff = self._compute_coefficients(h_data)
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energy_system = EnergySystem('{} capacity heat pump'.format(heat_pump.model), [])
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energy_system.air_source_hp = heat_pump
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self._city.add_city_object(energy_system)
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return self._city
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def _extract_heat_pump_data(self, heat_pump_capacity_data: Dict) -> [List, List]:
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"""
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Fetches a list of metric based data for heat pump for various temperature,
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eg. cooling capacity data for 12 capacity heat pump
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for 6,7,8,9,10 and 11 degree celsius
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:param heat_pump_capacity_data: the heat pump capacity data from the
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which the metric specific data is fetched: {List}
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:return: List
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"""
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cooling_heating_capacity_data = []
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compressor_power_data = []
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for _, metric_data in heat_pump_capacity_data.items():
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cooling_heating_capacity_data.append(metric_data[0])
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compressor_power_data.append(metric_data[1])
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return [cooling_heating_capacity_data, compressor_power_data]
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def _compute_coefficients(self, heat_pump_data: List, data_type="heat") -> List[float]:
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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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:param heat_pump_data: a list of heat pump data. eg. cooling capacity
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:param data_type: string to indicate if data is cooling performance data
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or heating performance data
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:return: Tuple[Dict, Dict]
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"""
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# Determine the recurrence of temperature values. 6 repetitions for
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# cooling performance and 5 repetition for heating performance
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temp_multiplier = 5 if data_type == "heat" else 6
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out_temp = [25, 30, 32, 35, 40, 45] * temp_multiplier
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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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x_values = heat_x_values if data_type == "heat" else cool_x_values
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x_values = x_values.tolist()
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# convert list of lists to one list
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hp_data = [i/j for i, j in
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zip(list(itertools.chain.from_iterable(heat_pump_data[0])),
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list(itertools.chain.from_iterable(heat_pump_data[1])))]
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# Compute heat output coefficients
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popt, _ = curve_fit(self._objective_function, [x_values, out_temp], hp_data)
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return popt.tolist()
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def _objective_function(self, xdata: List, a1: float, a2: float, a3: float, a4: float, a5: float, a6: float) -> float:
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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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