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EconomicsOfAHeatPump
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Refactored to a simple line diagram instead

master
Maximilian Stiefel 6 months ago
parent
4ad27aba58
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1e4ce25b85
2 changed files with 97 additions and 39 deletions
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  1. 8
      example_1.json
  2. 130
      heatpump.py

8
example_1.json
View File

@ -1,10 +1,10 @@
{
"currency": "SEK",
"legend": ["Installation price", "Heating expenditure"],
"technologies": [
{
"label": "Air water heat pump",
"installation_price": 120000,
"reinstallation_price": 120000,
"kwh_expenditure": 11000,
"kwh_price": 2,
"percent_inflation": 2,
@ -13,17 +13,19 @@
{
"label": "Geothermal heat pump",
"installation_price": 222788,
"reinstallation_price": 160000,
"kwh_expenditure": 5000,
"kwh_price": 2,
"percent_inflation": 2,
"years_lifespan": 25
"years_lifespan": 20
},
{
"label": "District heating",
"installation_price": 95133,
"reinstallation_price": 30000,
"kwh_expenditure": 25000,
"kwh_price": 0.95,
"percent_inflation": 10,
"percent_inflation": 8,
"years_lifespan": 50
}
]

130
heatpump.py
View File

@ -2,6 +2,7 @@ import matplotlib
matplotlib.use('Qt5Agg') # or 'Qt5Agg', depending on your setup
import matplotlib.pyplot as plt
import numpy as np
from numpy_da import DynamicArray
import json
import sys
import argparse
@ -13,80 +14,135 @@ class JsonSettingsExtractor:
with open(fname, "r") as rf:
settings = json.load(rf)
self._currency = settings["currency"]
self._legend = settings["legend"]
# Kind of inefficient code. Does not matter only runs once at startup
_technologies = settings["technologies"]
self._label = [technology["label"] for technology in _technologies]
self._installation_price = [technology["installation_price"] for technology in _technologies]
self._reinstallation_price = [technology["reinstallation_price"] for technology in _technologies]
self._kwh_expenditure = [technology["kwh_expenditure"] for technology in _technologies]
self._kwh_price = [technology["kwh_price"] for technology in _technologies]
self._percent_inflation = [technology["percent_inflation"] for technology in _technologies]
self._years_lifespan = [technology["years_lifespan"] for technology in _technologies]
self._amount_of_technologies = len(self.label)
@property
def currency(self):
return self._currency
@property
def legend(self):
return self._legend
@property
def label(self):
return self._label
@property
def installation_price(self):
return self._installation_price
return np.array(self._installation_price)
@property
def reinstallation_price(self):
return np.array(self._reinstallation_price)
@property
def kwh_expenditure(self):
return self._kwh_expenditure
return np.array(self._kwh_expenditure)
@property
def percent_inflation(self):
self._percent_inflation
return np.array(self._percent_inflation)
@property
def years_lifespan(self):
self._years_lifespan
return self._years_lifespan
@property
def amount_of_technologies(self):
return self._amount_of_technologies
def main():
parser = argparse.ArgumentParser(description='This script allows to calculate which heating technology makes sense financially for you.')
parser.add_argument('-a','--settings', help='Settings file.', required=True, metavar=('FILENAME'))
parser.add_argument('-g','--plot', help='Visualize costs over one or multiple years.', type=int, metavar=('YEARS'))
parser.add_argument('-b','--years', help='Amount of years for which to run the simulation.', required=True, type=int, metavar=('YEARS'))
parser.add_argument('-c','--plot', help='Visualize the calculations done.', action='store_true')
args = parser.parse_args()
input_data = JsonSettingsExtractor(args.settings)
print("Inflations: {}".format(input_data.percent_inflation))
print("Expend: {}".format(input_data.kwh_expenditure))
# if args.plot != None:
# # Basic settings
# width = 0.3
# plt_colors = ["#8ecae6", "#219ebc", "#023047", "#ffb703", "#fb8500"];
# fig, ax = plt.subplots()
# ax.bar(input_data.label, input_data.installation_price, width, label = "Installation price", color = plt_colors[0])
# current_y = input_data.installation_price
# years = 0 #first year
# # Iteration for years in service
# for i in range(args.plot):
# inflation_factor = 1.0 + (input_data.percent_inflation * years) / 100.0
# increase = input_data.kwh_expenditure * inflation_factor
# ax.bar(input_data.label, increase, width, bottom = current_y, label = "Expenditure".format(y), color = plt_colors[1])
# years += 1
# current_y += increase
# ax.set_ylabel(input_data.currency)
# ax.set_title("Comparision of economics w/ different heating technologies")
# ax.legend(input_data.legend)
# plt.show()
# Basic settings
currency = input_data.currency
# Initializations
year = 0 #first year
#grand_total = DynamicArray(shape=(0, input_data._amount_of_technologies))
grand_total = np.empty((0, input_data.amount_of_technologies))
# Iteration for years in service
for i in range(args.years):
# Initializations
increase_total = np.zeros(input_data.amount_of_technologies)
increase_installation = np.zeros(input_data.amount_of_technologies)
increase_expenditure = np.zeros(input_data.amount_of_technologies)
# Handle possible replacement of heating system
for j in range(len(input_data.years_lifespan)):
if (year % input_data.years_lifespan[j]) == 0 and year == 0:
increase_installation[j] = input_data.installation_price[j]
elif (year % input_data.years_lifespan[j]) == 0 and year > 0:
increase_installation[j] = input_data.reinstallation_price[j]
else:
increase_installation[j] = 0 # just to be clear
# Calculate increase
inflation_factor = np.array([1.0 + (x * year) / 100.0 for x in input_data.percent_inflation])
increase_expenditure = input_data.kwh_expenditure * inflation_factor
increase_total = increase_installation + increase_expenditure
# Safe the yearly costs for plotting
if year > 0:
grand_total = np.vstack((grand_total, increase_total + grand_total[year - 1]))
elif year == 0:
grand_total = np.vstack((grand_total, increase_total))
# Output data every year
# Rounding
increase_total = np.round(increase_total, decimals=0)
increase_installation = np.round(increase_installation, decimals=0)
increase_expenditure = np.round(increase_expenditure, decimals=0)
print(f"Year {year:2.0f} \t\tInstallation [{currency}] \tExpenditure [{currency}] \tTotal [{currency}] \tGrand total [{currency}]")
print("-------------------------------------------------------------------------------------------------------------")
for j in range(input_data.amount_of_technologies):
print(f"{input_data.label[j]} \t{increase_installation[j]:8.0f} \t\t{increase_expenditure[j]:8.0f} \t\t{increase_total[j]:8.0f} \t{grand_total[year][j]:8.0f}")
print("\n")
year += 1
if args.plot:
# Define colors for each line
plt_colors = ["#8ecae6", "#219ebc", "#023047", "#ffb703", "#fb8500"];
# Number of years and technologies
years = grand_total.shape[0]
technologies = grand_total.shape[1]
# Create a figure and axis
plt.figure(figsize=(10, 6))
# Generate line plots for each technology
for i in range(technologies):
plt.plot(range(years), grand_total[:, i], marker='o', color=plt_colors[i], label=f'Technology {i+1}')
# Adding titles and labels
plt.title('Economical comparision of different heating technologies')
plt.xlabel('Year')
plt.ylabel(f'Cost {currency}')
# plt.xticks(range(years), [f'Year {j+1}' for j in range(years)]) # Customize x-ticks if needed
plt.legend(input_data.label)
plt.grid()
# Show the plot
plt.tight_layout()
plt.show()
if __name__ == "__main__":
main()

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