Done with a simulation giving the most relevant answers

example_1.json

@@ -3,10 +3,12 @@
     "technologies": [	
         {
             "label": "Air water heat pump",
-            "installation_price": 120000,
+            "installation_price": 112568.75,
             "reinstallation_price": 120000,
-            "kwh_expenditure": 11000,
-            "kwh_price": 2,
+            "kwh_expenditure": 5453.4,
+            "kwh_price": 2.1,
+            "maintenance_price": 2000, 
+            "maintenance_interval": 2,
             "percent_inflation": 2,
             "years_lifespan": 15
         },
@@ -14,8 +16,10 @@
             "label": "Geothermal heat pump",
             "installation_price": 222788,
             "reinstallation_price": 160000,
-            "kwh_expenditure": 5000,
-            "kwh_price": 2,
+            "kwh_expenditure": 4519.1,
+            "kwh_price": 1.94,
+            "maintenance_price": 2000, 
+            "maintenance_interval": 2,
             "percent_inflation": 2,
             "years_lifespan": 20
         },
@@ -23,8 +27,10 @@
             "label": "District heating",
             "installation_price": 95133,
             "reinstallation_price": 30000,
-            "kwh_expenditure": 25000,
+            "kwh_expenditure": 18768,
             "kwh_price": 0.95,
+            "maintenance_price": 2000, 
+            "maintenance_interval": 2,
             "percent_inflation": 8,
             "years_lifespan": 50
         }

heatpump.py

@@ -21,6 +21,8 @@ class JsonSettingsExtractor:
             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._maintenance_price = [technology["maintenance_price"] for technology in _technologies]
+            self._maintenance_interval = [technology["maintenance_interval"] 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)
@@ -45,6 +47,14 @@ class JsonSettingsExtractor:
     def kwh_expenditure(self):
         return np.array(self._kwh_expenditure)
 
+    @property
+    def maintenance_price(self):
+        return self._maintenance_price
+
+    @property
+    def maintenance_interval(self):
+        return self._maintenance_interval
+
     @property
     def percent_inflation(self):
         return np.array(self._percent_inflation)
@@ -83,7 +93,7 @@ def main():
         increase_expenditure = np.zeros(input_data.amount_of_technologies)
 
         # Handle possible replacement of heating system
-        for j in range(len(input_data.years_lifespan)): 
+        for j in range(input_data.amount_of_technologies): 
             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:
@@ -91,9 +101,14 @@ def main():
             else: 
                 increase_installation[j] = 0 # just to be clear
         
+        # Handle possible maintenance
+        for j in range(input_data.amount_of_technologies): 
+            if (year % input_data.maintenance_interval[j]) == 0 and year != 0:
+                increase_expenditure[j] += input_data.maintenance_price[j]
+
         # 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_expenditure = input_data.kwh_expenditure * input_data._kwh_price * inflation_factor
         increase_total = increase_installation + increase_expenditure
 
         # Safe the yearly costs for plotting