diff --git a/ramp/core/core.py b/ramp/core/core.py
index 2e1308f8..5780ad0d 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -128,6 +128,19 @@ def add_appliance(self, *args, **kwargs):
         # I would add the appliance explicitely here, unless the appliance works only if a windows is defined
         return Appliance(self, *args, **kwargs)
 
+    @property
+    def windows_curve(self):
+        windows_curve = np.zeros(1440)
+        for App in self.App_list:
+            # Calculate windows curve, i.e. the theoretical maximum curve that can be obtained, for each app, by switching-on always all the 'n' apps altogether in any time-step of the functioning windows
+            single_wcurve = (
+                App.single_wcurve
+            )  # this computes the curve for the specific App
+            windows_curve = np.vstack(
+                [windows_curve, single_wcurve]
+            )  # this stacks the specific App curve in an overall curve comprising all the Apps within a User class
+        return np.transpose(np.sum(windows_curve, axis=0)) * self.num_users
+
     def save(self, filename=None):
         answer = pd.concat([app.save() for app in self.App_list], ignore_index=True)
         if filename is not None:
@@ -328,6 +341,10 @@ def windows(self, window_1 = np.array([0,0]), window_2 = np.array([0,0]),random_
         self.random_var_2 = int(random_var_w*np.diff(window_2)) #same as above
         self.random_var_3 = int(random_var_w*np.diff(window_3)) #same as above
         self.user.App_list.append(self) #automatically appends the appliance to the user's appliance list
+    @property
+    def single_wcurve(self):
+        return self.daily_use * np.mean(self.power) * self.number
+
 
         if self.fixed_cycle == 1:
             self.cw11 = self.window_1
diff --git a/ramp/core/stochastic_process.py b/ramp/core/stochastic_process.py
index ffb18c56..713ac6f5 100644
--- a/ramp/core/stochastic_process.py
+++ b/ramp/core/stochastic_process.py
@@ -24,14 +24,6 @@ def Stochastic_Process(j=None, fname=None, num_profiles=None):
     windows_curve = np.zeros(1440) #creates an empty daily profile
     Tot_curve = np.zeros(1440) #creates another empty daily profile
     for Us in User_list:
-        App_count = 0
-        for App in Us.App_list:
-            #Calculate windows curve, i.e. the theoretical maximum curve that can be obtained, for each app, by switching-on always all the 'n' apps altogether in any time-step of the functioning windows
-            single_wcurve = Us.App_list[App_count].daily_use*np.mean(Us.App_list[App_count].power)*Us.App_list[App_count].number #this computes the curve for the specific App
-            windows_curve = np.vstack([windows_curve, single_wcurve]) #this stacks the specific App curve in an overall curve comprising all the Apps within a User class
-            App_count += 1
-        Us.windows_curve = windows_curve #after having iterated for all the Apps within a User class, saves the overall User class theoretical maximum curve
-        Us.windows_curve = np.transpose(np.sum(Us.windows_curve, axis = 0))*Us.num_users
         Tot_curve = Tot_curve + Us.windows_curve #adds the User's theoretical max profile to the total theoretical max comprising all classes
     peak_window = np.transpose(np.argwhere(Tot_curve == np.amax(Tot_curve))) #Find the peak window within the theoretical max profile
     peak_time = round(random.normalvariate(round(np.average(peak_window)),1/3*(peak_window[0,-1]-peak_window[0,0]))) #Within the peak_window, randomly calculate the peak_time using a gaussian distribution