m3x1m0m
/
EconomicsOfAHeatPump
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Browse Source

Refactored to a simple line diagram instead

master
Maximilian Stiefel 1 year ago
parent
4ad27aba58
commit
1e4ce25b85
2 changed files with 97 additions and 39 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 8
      example_1.json
  2. 128
      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
}
]

128
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
@ -13,23 +14,20 @@ 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):
@ -37,56 +35,114 @@ class JsonSettingsExtractor:
@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])
# 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
# 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()
# 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()

Write Preview
Loading…
Cancel
Save