Added a module for importing water to water source heat pump
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Peter Yefi
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"""
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WaterToWaterHPParameters import the heat pump information
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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 pandas as pd
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from typing import Dict
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from city_model_structure.energy_systems.water_to_water_hp import WaterToWaterHP
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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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class WaterToWaterHPParameters:
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"""
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WaterToWaterHPParameters 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/water_to_water.xlsx')
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def _read_file(self) -> Dict:
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"""
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reads xlsx file containing water to water 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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hp_data = {}
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flow_rates = {
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'156': [2.84, 4.23, 5.68],
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'256': [4.73, 7.13, 9.446],
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'335': [6.62, 9.97, 12.93],
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}
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for sheet, dataframe in heat_pump_dfs.items():
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df = heat_pump_dfs[sheet].dropna(axis=1, how='all')
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df = df.iloc[3:, 6:35]
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if '156' in sheet:
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hp_data[sheet] = self._extract_required_hp_data(df, [0, 10, 25, 40, 55, 67], flow_rates['156'])
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elif '256' in sheet:
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hp_data[sheet] = self._extract_required_hp_data(df, [0, 9, 24, 39, 54, 66], flow_rates['256'])
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elif '335' in sheet:
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hp_data[sheet] = self._extract_required_hp_data(df, [0, 11, 26, 41, 56, 69], flow_rates['335'])
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return hp_data
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def _extract_required_hp_data(self, dataframe, ranges, flow_rates):
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"""
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Extracts 156 Kw water to water heat pump data
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:param dataframe: dataframe containing all data
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:param ranges: the range of values to extract
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:param flow_rates: the flow rates of water through pump
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:return: Dict
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"""
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# extract data rows and columns
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data = {'tc': self._extract_hp_data(dataframe, [1, 11, 21], ranges),
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'pd': self._extract_hp_data(dataframe, [2, 12, 22], ranges),
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'lwt': self._extract_hp_data(dataframe, [5, 15, 25], ranges),
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'fr': (self._extract_flow_and_ewt(dataframe, ranges, [1, 11, 21], flow_rates))[0],
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'ewt': (self._extract_flow_and_ewt(dataframe, ranges, [1, 11, 21], flow_rates))[1]}
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# range values for extracting data
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return data
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def _extract_hp_data(self, df, columns, ranges):
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"""
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Extract variable specific (LWT, PD or TC) data from water to water hp
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:param df: the dataframe
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:param columns: the columns to extract data from
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:param ranges: the range of values to extract
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:return: List
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"""
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data = df.iloc[ranges[0]:ranges[1], columns[0]].append(df.iloc[ranges[0]:ranges[1], columns[1]])\
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.append(df.iloc[ranges[0]:ranges[1], columns[2]])
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for i in range(1, 5):
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data = data.append(df.iloc[ranges[i]:ranges[i + 1], columns[0]]).append(
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df.iloc[ranges[i]:ranges[i + 1], columns[1]]).append(df.iloc[ranges[i]:ranges[i + 1], columns[2]])
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return data.dropna().values.tolist()
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def _extract_flow_and_ewt(self, df, ranges, columns, flow_rates):
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"""
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Create the flow and ewt data based on the length of the various
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columns for the variables being extracted
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:param df: the dataframe
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:param ranges: the range of values to extract
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:param columns: the columns to extract data from
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:param flow_rates: flow rate values
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:return:
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"""
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ewt_values = [-1.111111111, 4.444444444, 10, 15.55555556, 21.11111111]
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length = [len(df.iloc[ranges[0]:ranges[1], columns[0]].dropna()),
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len(df.iloc[ranges[0]:ranges[1], columns[1]].dropna()),
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len(df.iloc[ranges[0]:ranges[1], columns[2]].dropna())]
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ewt_data = np.repeat(ewt_values[0], sum(length))
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flow_rates_data = np.repeat(flow_rates, length)
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for i in range(1, 5):
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length = [len(df.iloc[ranges[i]:ranges[i + 1], columns[0]].dropna()),
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len(df.iloc[ranges[i]:ranges[i + 1], columns[1]].dropna()),
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len(df.iloc[ranges[i]:ranges[i + 1], columns[2]].dropna())]
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flow_rates_data = np.append(flow_rates_data, np.repeat(flow_rates, length))
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ewt_data = np.append(ewt_data, np.repeat(ewt_values[i], sum(length)))
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return flow_rates_data.tolist(), ewt_data.tolist()
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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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heap_pump_data = self._read_file()
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for model, data in heap_pump_data.items():
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heat_pump = WaterToWaterHP()
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heat_pump.model = model.strip()
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heat_pump.total_cooling_capacity = data['tc']
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heat_pump.power_demand = data['pd']
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heat_pump.flow_rate = data['fr']
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heat_pump.entering_water_temp = data['ewt']
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heat_pump.leaving_water_temp = data['lwt']
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heat_pump.power_demand_coff = self._compute_coefficients(data, data_type='power')
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heat_pump.total_cooling_capacity_coff = self._compute_coefficients(data)
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energy_system = EnergySystem(heat_pump.model, 0, [], None)
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energy_system.water_to_water_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 _compute_coefficients(self, heat_pump_data: Dict, data_type="heat_output") -> List[float]:
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"""
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Compute heat output and electrical demand coefficients
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from heating performance data
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:param heat_pump_data: a dictionary of heat pump data.
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:param data_type: string to indicate whether coefficient are power demands
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or heat output coefficients. Default is heat output
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:return: Tuple[Dict, Dict]
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"""
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demand = heat_pump_data['tc'] if data_type == "heat_output" else heat_pump_data['pd']
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# Compute heat output coefficients
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popt, _ = curve_fit(self._objective_function, [heat_pump_data['ewt'], heat_pump_data['lwt'], heat_pump_data['fr']],
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demand)
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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,
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a7: float, a8: float, a9: float, a10: float, a11: 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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:param a7: float
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:param a8: float
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:param a9: float
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:param a10: float
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:param a11: float
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:return:
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"""
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x, y, t = xdata
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return (a1 * x ** 2) + (a2 * x) + (a3 * y ** 2) + (a4 * y) + (a5 * t ** 2) + (a6 * t) + (a7 * x * y) + (
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a8 * x * t) + (a9 * y * t) + (a10 * x * y * t) + a11
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