diff --git a/docs/source/examples/cooking_app/cooking_app.rst b/docs/source/examples/cooking_app/cooking_app.rst
index 1ccd5c6d..670a09c1 100644
--- a/docs/source/examples/cooking_app/cooking_app.rst
+++ b/docs/source/examples/cooking_app/cooking_app.rst
@@ -4,19 +4,19 @@ Cooking Appliances
 In this example, appliances with multiple preferences index and
 attributes are modelled.
 
-To have a better understanding of RAMP features for modelling these
+To have a better understanding of RAMP features for modelling this
 category of appliances, two households are considered:
 
-1. A household with a fixed lunch habit of eating soup everyday.
+1. A household with a fixed lunch habit of eating soup every day.
 2. A household with two lunch preferences: cooking soup or rice.
 
-The number of user preferences can be specified through the
+The number of user preferences can be specified through
 **“user_preference”** parameter when initializing a **User** instance.
 
 .. code:: ipython3
 
     # importing functions
-    from ramp import User,UseCase,calc_peak_time_range,yearly_pattern
+    from ramp import User, UseCase, get_day_type
     import pandas as pd
 
 Creating a user category
@@ -25,21 +25,21 @@ Creating a user category
 .. code:: ipython3
 
     user_1 = User(
-        user_name = "Household with a single lunch habit",
-        num_users = 1,
-        user_preference = 1, # user_1 has only one lunch preference
+        user_name="Household with single lunch habit",
+        num_users=1,
+        user_preference=1,  # user_1 has only one lunch preference
     )
     
     user_2 = User(
-        user_name = "Household with different lunch habit",
-        num_users = 1,
-        user_preference = 2, # user_2 has two lunch preferences
+        user_name="Household with different lunch habits",
+        num_users=1,
+        user_preference=2,  # user_2 has two lunch preferences
     )
 
 Defining the cycles for cooking soup and rice
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-For cooking soup, we assume the user needs 25 minutes divided
+For cooking soup it is assumed that the user needs 25 minutes divided
 into two parts:
 
 ============= ===== ====
@@ -49,7 +49,7 @@ Boiling Water 1200  5
 Cooking soup  750   20
 ============= ===== ====
 
-For cooking rice, we assume the user needs 15 minutes divided
+For cooking rice it is assumed that the user needs 15 minutes divided
 into two parts:
 
 ============= ===== ====
@@ -63,77 +63,75 @@ Cooking rice  600   10
 
     # soup for lunch
     soup_1 = user_1.add_appliance(
-        name = "soup for lunch",
-        power = 1200,
-        func_time = 25,
-        func_cycle = 25,
-        thermal_p_var = 0.2,
-        fixed_cycle = 1,
-        window_1 = [12*60,15*60],
-        p_11 = 1200,   # power of the first cycle
-        t_11 = 5,      # time needed for the first cycle
-        p_12 = 750,    # power of the second cycle
-        t_12 = 20,     # time needed for the second cycle
-        cw11 = [12*60,15*60]
+        name="soup for lunch",
+        power=1200,
+        func_time=25,
+        func_cycle=25,
+        thermal_p_var=0.2,
+        fixed_cycle=1,
+        window_1=[12 * 60, 15 * 60],
+        p_11=1200,  # power of the first cycle
+        t_11=5,  # time needed for the first cycle
+        p_12=750,  # power of the second cycle
+        t_12=20,  # time needed for the second cycle
+        cw11=[12 * 60, 15 * 60],
     )
 
+The second user has two different preferences for lunch. Accordingly, we
+need to model these preferences and their characteristics as two
+different appliances.
 
-The second user has two different preferences for lunch. Accordingly, we need to model
-these preferences and their characterisitics as two different appliances.
-
-Each preference needs to be specified with its associated cooking energy needs, such as
-the power, functioning time and the duty cycles of the cooking process.
+Each preference needs to be specified with its associated cooking energy
+needs, such as the power, functioning time and the duty cycles of the
+cooking process.
 
 More importantly, for each preference, the user needs to specify the
-index of preference by using the **pref_index** parameter. In this example,
-soup is the first preference of the user (pref_index = 1), and rice is
-the second one (pref_index = 2).
+index of preference by using the **pref_index** parameter. In this
+example, soup is the first preference of the user (pref_index = 1), and
+rice is the second one (pref_index = 2).
 
 .. code:: ipython3
 
     # soup for lunch
     soup_2 = user_2.add_appliance(
-        name = "soup for lunch",
-        power = 1200,
-        func_time = 25,
-        func_cycle = 25,
-        thermal_p_var = 0.2,
-        fixed_cycle = 1,
-        pref_index = 1, # the first preference
-        window_1 = [12*60,15*60],
-        p_11 = 1200,   # power of the first cycle
-        t_11 = 5,      # time needed for the first cycle
-        p_12 = 750,    # power of the second cycle
-        t_12 = 20,     # time needed for the second cycle
-        cw11 = [12*60,15*60]
+        name="soup for lunch",
+        power=1200,
+        func_time=25,
+        func_cycle=25,
+        thermal_p_var=0.2,
+        fixed_cycle=1,
+        pref_index=1,  # the first preference
+        window_1=[12 * 60, 15 * 60],
+        p_11=1200,  # power of the first cycle
+        t_11=5,  # time needed for the first cycle
+        p_12=750,  # power of the second cycle
+        t_12=20,  # time needed for the second cycle
+        cw11=[12 * 60, 15 * 60],
     )
-    
-
 
 .. code:: ipython3
 
     # rice for lunch
     rice_2 = user_2.add_appliance(
-        name = "rice for lunch",
-        power = 1200,
-        func_time = 15,
-        func_cycle = 15,
-        thermal_p_var = 0.2,
-        pref_index = 2,  # the second preference
-        fixed_cycle = 1,
-        window_1 = [12*60,15*60],
-        p_11 = 1200,   # power of the first cycle
-        t_11 = 5,      # time needed for the first cycle
-        p_12 = 600,    # power of the second cycle
-        t_12 = 10,     # time needed for the second cycle
-        cw11 = [12*60,15*60]
-        
+        name="rice for lunch",
+        power=1200,
+        func_time=15,
+        func_cycle=15,
+        thermal_p_var=0.2,
+        pref_index=2,  # the second preference
+        fixed_cycle=1,
+        window_1=[12 * 60, 15 * 60],
+        p_11=1200,  # power of the first cycle
+        t_11=5,  # time needed for the first cycle
+        p_12=600,  # power of the second cycle
+        t_12=10,  # time needed for the second cycle
+        cw11=[12 * 60, 15 * 60],
     )
 
 .. code:: ipython3
 
-    # you can have an overview of the just defined input data by using User.export_to_dataframe method
-    user_lunch = UseCase(users=[user_1,user_2])
+    # you can have an overview of data inputs by usering User.export_to_dataframe method
+    user_lunch = UseCase(users=[user_1, user_2], date_start="2020-01-01")
     user_lunch.export_to_dataframe().T
 
 
@@ -167,7 +165,7 @@ the second one (pref_index = 2).
       <tbody>
         <tr>
           <th>user_name</th>
-          <td>Household with a single lunch habit</td>
+          <td>Household with single lunch habit</td>
           <td>Household with different lunch habit</td>
           <td>Household with different lunch habit</td>
         </tr>
@@ -480,58 +478,58 @@ the second one (pref_index = 2).
 Generating a profile for some months
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. code:: ipython3
-
-    peak_time_range = calc_peak_time_range(
-        user_list = user_lunch.users
-    )
-    year_behaviour = yearly_pattern()
-
-
 .. code:: ipython3
 
     # number of days
     n_days = 90
     
+    user_lunch.initialize(num_days=n_days)
     # storing all the profiles for all the users
-    profiles = pd.DataFrame(index = pd.date_range(start = "2020-01-01",periods = 1440*n_days,freq="T"))
+    profiles = pd.DataFrame(
+        index=pd.date_range(start="2020-01-01", periods=1440 * n_days, freq="T")
+    )
+    
+    # here we need to use the
     
     for user in user_lunch.users:
-       
         # storing daily profiles for a user
         user_profiles = []
-        for day in range(n_days):
+        for day_idx, day in enumerate(user_lunch.days):
             single_profile = user.generate_single_load_profile(
-                prof_i = day, # the day to generate the profile
-                peak_time_range = peak_time_range,
-                Year_behaviour = year_behaviour
+                prof_i=day_idx,  # the day to generate the profile
+                day_type=get_day_type(day),
             )
     
             user_profiles.extend(single_profile)
-            
+    
         profiles[user.user_name] = user_profiles
-            
-        
 
 
-Considering that the second user has the possibility of cooking rice for lunch, which 
-has a less energy-intensive cooking cycle, we expect to see a higher energy 
-consumption for the the user that only eats soup, in most of the cases.
+.. parsed-literal::
+
+    You will simulate 90 day(s) from 2020-01-01 00:00:00 until 2020-03-31 00:00:00
+
+
+Considering that the second user has the possibility of cooking rice for
+lunch, which has a less energy-intensive cooking cycle, we expect to see
+a higher energy consumption for the user that only eats soup, in most of
+the cases.
 
 .. code:: ipython3
 
     # daily energy consumption
-    profiles.resample("1d").sum().plot(title = "daily energy consumption")
+    profiles.resample("1d").sum().plot(title="daily energy consumption")
 
 
 
 
 .. parsed-literal::
 
-    <AxesSubplot:title={'center':'daily energy consumption'}>
+    <Axes: title={'center': 'daily energy consumption'}>
+
 
 
 
+.. image:: output_13_1.png
 
-.. image:: output_14_1.png
 
diff --git a/docs/source/examples/fixed_flat_app/fixed_flat_app.rst b/docs/source/examples/fixed_flat_app/fixed_flat_app.rst
index 1d624308..12f01860 100644
--- a/docs/source/examples/fixed_flat_app/fixed_flat_app.rst
+++ b/docs/source/examples/fixed_flat_app/fixed_flat_app.rst
@@ -4,7 +4,7 @@ Fixed-Flat Appliance
 .. code:: ipython3
 
     # importing functions
-    from ramp import User,calc_peak_time_range,yearly_pattern
+    from ramp import User, UseCase, get_day_type
     import pandas as pd
 
 Creating a user
@@ -12,10 +12,7 @@ Creating a user
 
 .. code:: ipython3
 
-    school = User(
-        user_name = "School",
-        num_users = 1
-    )
+    school = User(user_name="School", num_users=1)
 
 Adding an appliance with flat and fixed consumption
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -23,62 +20,63 @@ Adding an appliance with flat and fixed consumption
 .. code:: ipython3
 
     indoor_bulb = school.add_appliance(
-        name = "Indoor Light Bulb",
-        number = 10,
-        power  = 25,
-        num_windows = 1,
-        func_time = 210,
-        time_fraction_random_variability = 0.2,
-        func_cycle = 60,
-        fixed = "yes",
-        flat = "yes",
-        
+        name="Indoor Light Bulb",
+        number=10,
+        power=25,
+        num_windows=1,
+        func_time=210,
+        time_fraction_random_variability=0.2,
+        func_cycle=60,
+        fixed="yes", # This means all the 'n' appliances of this kind are always switched-on together
+        flat="yes", # This means the appliance is not subject to random variability in terms of total usage time
     )
     indoor_bulb.windows(
-        window_1 = [1200,1440], # from 20:00 to 24:00
-        window_2 = [0,0], 
-        random_var_w = 0.35,
+        window_1=[1200, 1440],  # from 20:00 to 24:00
+        window_2=[0, 0],
+        random_var_w=0.35,
     )
 
-Defining the peak time range using the calc_peak_time_range
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Initialize the usecase (it defines the peak time range and simulation time)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. code:: ipython3
 
-    peak_time_range = calc_peak_time_range(
-        user_list = [school]
-    )
+    school_case = UseCase(users=[school], date_start="2023-01-01")
+    school_case.initialize(num_days=7)
 
-Defining the yearly patterns
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-By default the :code:`yearly_pattern` function returns weekdays (defined by 0)
-/weekends (defined by 1) division
+.. parsed-literal::
 
-.. code:: ipython3
+    You will simulate 7 day(s) from 2023-01-01 00:00:00 until 2023-01-08 00:00:00
 
-    year_behaviour = yearly_pattern()
 
 Generating a profile for the 1st week of the year
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+From the usecase directly
+
+.. code:: ipython3
+
+    first_week = school_case.generate_daily_load_profiles(flat=True)
+
+or from the user
+
 .. code:: ipython3
 
     first_week = []
     
-    for i in range(7):
+    for day_idx, day in enumerate(school_case.days):
         first_week.extend(
             school.generate_single_load_profile(
-                prof_i = i, # the day to generate the profile
-                peak_time_range = peak_time_range,
-                Year_behaviour = year_behaviour
+                prof_i=day_idx,  # the day to generate the profile
+                peak_time_range=school_case.peak_time_range,
+                day_type=get_day_type(day),
             )
         )
-        
 
 .. code:: ipython3
 
-    first_week = pd.DataFrame(first_week,columns=["household"])
+    first_week = pd.DataFrame(first_week, columns=["household"])
     first_week.plot()
 
 
@@ -86,10 +84,11 @@ Generating a profile for the 1st week of the year
 
 .. parsed-literal::
 
-    <AxesSubplot:>
+    <Axes: >
 
 
 
 
 .. image:: output_12_1.png
 
+
diff --git a/docs/source/examples/fixed_flat_app/output_12_1.png b/docs/source/examples/fixed_flat_app/output_12_1.png
index 7b52a6d4..3c0fe8a9 100644
Binary files a/docs/source/examples/fixed_flat_app/output_12_1.png and b/docs/source/examples/fixed_flat_app/output_12_1.png differ
diff --git a/docs/source/examples/multi_cycle/multi_cycle.rst b/docs/source/examples/multi_cycle/multi_cycle.rst
index 4e8d42d8..f03e52e0 100644
--- a/docs/source/examples/multi_cycle/multi_cycle.rst
+++ b/docs/source/examples/multi_cycle/multi_cycle.rst
@@ -2,13 +2,13 @@ Appliances with multiple cycles
 ===============================
 
 An example of an appliance with multiple cycle is a fridge. Fridges
-usually have different duty cycles, which can be estimated based on seasonal
-temperature trends and/or frequency of user interaction (e.g., how often the 
-door gets opened).
+usually have different duty cycles, which can be estimated based on
+seasonal temperature trends and/or frequency of user interaction (e.g.,
+how often the door gets opened).
 
 In this example a fridge with 3 different duty cycles is modelled. The
-time windows are defined for 3 different cycles across 3 different season
-types:
+time windows are defined for 3 different cycles across 3 different
+season types:
 
 +--------+------------------------------+--------------+--------------+
 | season | Standard cycle               | Intermediate | Intensive    |
@@ -30,7 +30,7 @@ Creating the user and appliance
 .. code:: ipython3
 
     # importing functions
-    from ramp import User,calc_peak_time_range,yearly_pattern
+    from ramp import User, UseCase, get_day_type
     import pandas as pd
 
 .. code:: ipython3
@@ -42,22 +42,21 @@ Creating the user and appliance
 
     # creating the appliance
     fridge = household.Appliance(
-        name = "Fridge",
-        number = 1,
-        power  = 200,
-        num_windows = 1,
-        func_time = 1400,
-        time_fraction_random_variability = 0,
-        func_cycle = 30,
-        fixed = "yes",
-        fixed_cycle = 3, # number of cycles
+        name="Fridge",
+        number=1,
+        power=200,
+        num_windows=1,
+        func_time=1400,
+        time_fraction_random_variability=0,
+        func_cycle=30,
+        fixed="yes",
+        fixed_cycle=3,  # number of cycles
     )
 
 .. code:: ipython3
 
     # setting the functioning windows
-    fridge.windows([0,1440]) # always on during the whole year
-
+    fridge.windows([0, 1440])  # always on during the whole year
 
 Assigining the specific cycles
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -67,26 +66,26 @@ Assigining the specific cycles
     # assiging the specific cycles
     # first cycle: standard cycle
     fridge.specific_cycle_1(
-        p_11 = 200,
-        t_11 = 20,
-        p_12 = 5,
-        t_12 = 10,
+        p_11=200,
+        t_11=20,
+        p_12=5,
+        t_12=10,
     )
     
     # second cycle: intermediate cycle
     fridge.specific_cycle_2(
-        p_21 = 200,
-        t_21 = 15,
-        p_22 = 5,
-        t_22 = 15,
+        p_21=200,
+        t_21=15,
+        p_22=5,
+        t_22=15,
     )
     
     # third cycle: intensive cycle
     fridge.specific_cycle_3(
-        p_31 = 200,
-        t_31 = 10,
-        p_32 = 5,
-        t_32 = 20,
+        p_31=200,
+        t_31=10,
+        p_32=5,
+        t_32=20,
     )
 
 After defining the cycle power and duration parameters, the time windows
@@ -96,10 +95,7 @@ of year at which the cycles happens should be specifid by:
 
     # defining cycle behaviour
     fridge.cycle_behaviour(
-        cw11 = [480,1200],
-        cw21 = [300,479],
-        cw31 = [0,229],
-        cw32 = [1201,1440]
+        cw11=[480, 1200], cw21=[300, 479], cw31=[0, 229], cw32=[1201, 1440]
     )
 
 Buidling the profiles
@@ -107,29 +103,27 @@ Buidling the profiles
 
 .. code:: ipython3
 
-    peak_time_range = calc_peak_time_range(
-        user_list = [household]
-    )
-    year_behaviour = yearly_pattern()
+    use_case = UseCase(users=[household])
+    peak_time_range = use_case.calc_peak_time_range()
 
 .. code:: ipython3
 
     # days to build the profiles
-    days = {
-        "May-16": 136,
-        "August-16": 228,
-        "December-16": 350,
-    }
+    days = [
+        "2020-05-16",
+        "2020-08-16",
+        "2020-12-16",
+    ]
     
-    profiles = pd.DataFrame(index=range(0,1440),columns = days.keys())
+    profiles = pd.DataFrame(index=range(0, 1440), columns=days)
     
-    for day,i in days.items():
+    for day_idx, day in enumerate(days):
         profile = household.generate_single_load_profile(
-            prof_i = i, # the day to generate the profile
-            peak_time_range = peak_time_range,
-            Year_behaviour = year_behaviour
+            prof_i=day_idx,  # the day to generate the profile
+            peak_time_range=peak_time_range,
+            day_type=get_day_type(day),
         )
-        
+    
         profiles[day] = profile
 
 .. code:: ipython3
@@ -139,18 +133,18 @@ Buidling the profiles
 
 .. parsed-literal::
 
-          May-16  August-16  December-16
-    0      0.001      0.001        0.001
-    1      0.001      0.001        0.001
-    2      0.001      5.000        0.001
-    3      0.001      5.000        0.001
-    4      0.001      5.000        0.001
-    ...      ...        ...          ...
-    1435   5.000      5.000      200.000
-    1436   5.000    200.000      200.000
-    1437   5.000    200.000      200.000
-    1438   5.000    200.000      200.000
-    1439   5.000    200.000      200.000
+          2020-05-16  2020-08-16  2020-12-16
+    0          0.001       5.000       0.001
+    1          0.001       5.000       0.001
+    2          0.001       5.000       0.001
+    3          5.000       5.000       0.001
+    4          5.000       5.000       0.001
+    ...          ...         ...         ...
+    1435       0.001       0.001       0.001
+    1436       0.001       0.001       0.001
+    1437       0.001       0.001       0.001
+    1438       0.001       0.001       0.001
+    1439       0.001       0.001       0.001
     
     [1440 rows x 3 columns]
 
diff --git a/docs/source/examples/multi_cycle/output_15_1.png b/docs/source/examples/multi_cycle/output_15_1.png
index 4d30f9af..e74e5e89 100644
Binary files a/docs/source/examples/multi_cycle/output_15_1.png and b/docs/source/examples/multi_cycle/output_15_1.png differ
diff --git a/docs/source/examples/occasional_use/occasional_use.rst b/docs/source/examples/occasional_use/occasional_use.rst
index fda03849..ad9be5a6 100644
--- a/docs/source/examples/occasional_use/occasional_use.rst
+++ b/docs/source/examples/occasional_use/occasional_use.rst
@@ -1,29 +1,29 @@
 Appliances with occasional use
 ==============================
 
-There are some appliances that are occasionally included in the mix pf
-appliances that the user switches-on during the day. For example, an iron,
-a stereo, printers, etc.
+There are some appliances that are occasionally included in the mix of
+appliances that the user switches on during the day. For example, an
+iron, a stereo, printers, etc.
 
 Within RAMP, the user may specify the probability of using an appliance
-on the daily mix with a parameter called, **occasional_use**.
+on the daily mix with a parameter called **occasional_use**.
 
-When :code:`occasional_use = 0`, the appliance is always present in the mix, and
-when :code:`occasional_use = 1`,  the appliance is never present. Any in-between
-values will lead to a probablistic calculation to decide whether the appliance
-is used or not on a given day.
+When ``occasional_use = 0``, the appliance is always present in the mix,
+and when ``occasional_use = 1``, the appliance is never present. Any
+in-between values will lead to a probabilistic calculation to decide
+whether the appliance is used or not on a given day.
 
-The following example, investigates the effect of this parameter by
-modelling two user category: \* A household that uses a computer with
-ocationally \* A school that uses the computer everyday
+The following example investigates the effect of this parameter by
+modelling two user categories: \* A household that uses a computer
+occasionally \* A school that uses the computer every day
 
 .. code:: ipython3
 
     # importing functions
-    from ramp import User,calc_peak_time_range,yearly_pattern
+    from ramp import User, UseCase, get_day_type
     import pandas as pd
 
-Creating a user category and appliances
+Creating user categories and appliances
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. code:: ipython3
@@ -34,38 +34,35 @@ Creating a user category and appliances
 .. code:: ipython3
 
     computer_0 = household.add_appliance(
-        name = "Household Computer",
-        number = 1,
-        power  = 50,
-        num_windows = 1,
-        func_time = 210,
-        occasional_use = 0.5,  # 50% chance of occasional use,
-        window_1 = [510,750],
+        name="Household Computer",
+        number=1,
+        power=50,
+        num_windows=1,
+        func_time=210,
+        occasional_use=0.5,  # 50% chance of occasional use,
+        window_1=[510, 750],
     )
 
-
 .. code:: ipython3
 
     computer_1 = school.add_appliance(
-        name = "School Computer",
-        number = 1,
-        power  = 50,
-        num_windows = 1,
-        func_time = 210,
-        time_fraction_random_variability = 0.2,
-        func_cycle = 10,
-        occasional_use = 1, # always present in the mix of appliances,
-        window_1 = [510,750],
+        name="School Computer",
+        number=1,
+        power=50,
+        num_windows=1,
+        func_time=210,
+        time_fraction_random_variability=0.2,
+        func_cycle=10,
+        occasional_use=1,  # always present in the mix of appliances,
+        window_1=[510, 750],
     )
-    
-
 
 .. code:: ipython3
 
     # Checking the maximum profile of the two appliances
     
-    max_profile_c1 = pd.DataFrame(computer_0.maximum_profile,columns=[computer_0.name])
-    max_profile_c2 = pd.DataFrame(computer_1.maximum_profile,columns=[computer_1.name])
+    max_profile_c1 = pd.DataFrame(computer_0.maximum_profile, columns=[computer_0.name])
+    max_profile_c2 = pd.DataFrame(computer_1.maximum_profile, columns=[computer_1.name])
     
     max_profile_c1.plot()
     max_profile_c2.plot()
@@ -75,7 +72,7 @@ Creating a user category and appliances
 
 .. parsed-literal::
 
-    <AxesSubplot:>
+    <Axes: >
 
 
 
@@ -92,36 +89,31 @@ Generating profiles
 
 .. code:: ipython3
 
-    peak_time_range = calc_peak_time_range(
-        user_list = [household,school]
-    )
-    year_behaviour = yearly_pattern()
+    use_case = UseCase(users=[household, school])
+    use_case.initialize(5)
+
+
+.. parsed-literal::
+
+    You will simulate 5 day(s) from 2023-12-01 00:00:00 until 2023-12-06 00:00:00
+
 
 .. code:: ipython3
 
-    # plotting profiles for 5 days
-    days = range(10)
-    
-    for day in days:
+    for day_idx, day in enumerate(use_case.days):
         household_profile = household.generate_single_load_profile(
-            prof_i = day, 
-            peak_time_range = peak_time_range,
-            Year_behaviour = year_behaviour
+            prof_i=day_idx, day_type=get_day_type(day)
         )
-        
+    
         school_profile = school.generate_single_load_profile(
-            prof_i = day, 
-            peak_time_range = peak_time_range,
-            Year_behaviour = year_behaviour
+            prof_i=day_idx, day_type=get_day_type(day)
         )
-        
+    
         pd.DataFrame(
-            data = [household_profile,school_profile],
-            columns = range(1440),
-            index = [household.user_name,school.user_name]
+            data=[household_profile, school_profile],
+            columns=range(1440),
+            index=[household.user_name, school.user_name],
         ).T.plot(title=f"day - {day}")
-        
-        
 
 
 
@@ -144,26 +136,6 @@ Generating profiles
 .. image:: output_9_4.png
 
 
-
-.. image:: output_9_5.png
-
-
-
-.. image:: output_9_6.png
-
-
-
-.. image:: output_9_7.png
-
-
-
-.. image:: output_9_8.png
-
-
-
-.. image:: output_9_9.png
-
-
 As it can be seen from the figures, the computer is always present in
-the school's appliance mix while, for the household, it is only occasionally 
-present.
+the school’s appliance mix while, for the household, it is only
+occasionally present.
diff --git a/docs/source/examples/occasional_use/output_6_1.png b/docs/source/examples/occasional_use/output_6_1.png
index c9f2fce9..00924f12 100644
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diff --git a/docs/source/examples/occasional_use/output_6_2.png b/docs/source/examples/occasional_use/output_6_2.png
index 2a40a2ca..322044ae 100644
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diff --git a/docs/source/examples/occasional_use/output_9_0.png b/docs/source/examples/occasional_use/output_9_0.png
index 8ac7d21f..32d15e93 100644
Binary files a/docs/source/examples/occasional_use/output_9_0.png and b/docs/source/examples/occasional_use/output_9_0.png differ
diff --git a/docs/source/examples/occasional_use/output_9_1.png b/docs/source/examples/occasional_use/output_9_1.png
index 64a16ded..4587091a 100644
Binary files a/docs/source/examples/occasional_use/output_9_1.png and b/docs/source/examples/occasional_use/output_9_1.png differ
diff --git a/docs/source/examples/occasional_use/output_9_2.png b/docs/source/examples/occasional_use/output_9_2.png
index c6251df7..4f3c7049 100644
Binary files a/docs/source/examples/occasional_use/output_9_2.png and b/docs/source/examples/occasional_use/output_9_2.png differ
diff --git a/docs/source/examples/occasional_use/output_9_3.png b/docs/source/examples/occasional_use/output_9_3.png
index 0ee1781c..940801e2 100644
Binary files a/docs/source/examples/occasional_use/output_9_3.png and b/docs/source/examples/occasional_use/output_9_3.png differ
diff --git a/docs/source/examples/occasional_use/output_9_4.png b/docs/source/examples/occasional_use/output_9_4.png
index a5074c36..1b03b3b5 100644
Binary files a/docs/source/examples/occasional_use/output_9_4.png and b/docs/source/examples/occasional_use/output_9_4.png differ
diff --git a/docs/source/examples/parallel_processing/parallel_processing.rst b/docs/source/examples/parallel_processing/parallel_processing.rst
index 22891b1c..43571111 100644
--- a/docs/source/examples/parallel_processing/parallel_processing.rst
+++ b/docs/source/examples/parallel_processing/parallel_processing.rst
@@ -1,138 +1,156 @@
 Parallel processing
 ===================
 
-There is possibility to generate usecases profiles at the appliance
-level using parallel processing.
+There is the possibility of generating UseCase profiles using parallel
+processing.
 
-For example to generate 365 profiles using a parallel process in shell
-use the ``-p`` option
+For example, to generate 365 profiles using a parallel process in shell,
+use the ``-p`` option.
 
-.. code:: shell
-
-    ramp -i <path to your input file> -p -n 365
+ramp -i <path to your input file> -p -n 365
 
 The following cells provide you with a way to use the parallel process
-in a pure python code
+in a pure python code.
 
 .. code:: ipython3
 
     from ramp import UseCase
     
     import numpy as np
-    import random 
+    import random
     import math
     import pandas as pd
-    from ramp.core.initialise import initialise_inputs
     from ramp.core.utils import calc_time_taken, get_day_type
-    from ramp.core.core import User, UseCase
-    from ramp.core.stochastic_process import calc_peak_time_range
+    from ramp import User, UseCase
     from ramp.post_process import post_process as pp
     
     
-    uc = UseCase() 
+    use_case = UseCase(date_start="2022-01-01", date_end="2022-01-09", peak_enlarge=0.15)
     household = User(
-        user_name = "Household",
-        num_users = 10,
+        user_name="Household",
+        num_users=10,
     )
     indoor_bulb = household.add_appliance(
-        name = "Indoor Light Bulb",
-        number = 6,
-        power  = 7,
-        num_windows = 2,
-        func_time = 120,
-        time_fraction_random_variability = 0.2,
-        func_cycle = 10,
-        window_1 = [1170,1440], # from 19:30 to 24:00
-        window_2 = [0,30], # from 24 to 00:30
-        random_var_w = 0.35,
+        name="Indoor Light Bulb",
+        number=6,
+        power=7,
+        num_windows=2,
+        func_time=120,
+        time_fraction_random_variability=0.2,
+        func_cycle=10,
+        window_1=[1170, 1440],  # from 19:30 to 24:00
+        window_2=[0, 30],  # from 24 to 00:30
+        random_var_w=0.35,
     )
     
-    uc.add_user(household)
-    
-    num_profiles = 1
-    Profiles_list = []
-    
-    days = pd.date_range(start="2022-01-01", end="2022-01-09")
-    
-    peak_enlarge = 0.15
-    
-    # Calculation of the peak time range, which is used to discriminate between off-peak
-    # and on-peak coincident switch-on probability, corresponds to step 1. of [1], p.6
-    peak_time_range = calc_peak_time_range(uc.users, peak_enlarge)
-    
+    use_case.add_user(household)
+
+
+.. parsed-literal::
 
+    You will simulate 9 day(s) from 2022-01-01 00:00:00 until 2022-01-10 00:00:00
 
-Run usecase without parallel processing
+
+Run UseCase without parallel processing
 ---------------------------------------
 
 .. code:: ipython3
 
-    for day in days:
-        print("Day", day)
+    daily_profiles = use_case.generate_daily_load_profiles(flat=False, verbose=True)
     
-        # without // processing
-        daily_profiles = uc.generate_daily_load_profiles(num_profiles, peak_time_range, get_day_type(day))
+    # Post-processes the results and generates plots
+    Profiles_avg, Profiles_list_kW, Profiles_series = pp.Profile_formatting(daily_profiles)
+    pp.Profile_series_plot(Profiles_series)  # by default, profiles are plotted as a series
     
-        Profiles_list.append(np.mean(daily_profiles, axis=0))
+    if (
+        len(daily_profiles) > 1
+    ):  # if more than one daily profile is generated, also cloud plots are shown
+        pp.Profile_cloud_plot(daily_profiles, Profiles_avg)
+
+
+.. parsed-literal::
+
+    Day 1 / 9 completed
+    Day 2 / 9 completed
+    Day 3 / 9 completed
+    Day 4 / 9 completed
+    Day 5 / 9 completed
+    Day 6 / 9 completed
+    Day 7 / 9 completed
+    Day 8 / 9 completed
+    Day 9 / 9 completed
+
+
+
+.. image:: output_6_1.png
+
+
+
+.. image:: output_6_2.png
+
+
+Run UseCase with parallel processing
+------------------------------------
+
+Explicitly calling the parallel method
+
+.. code:: ipython3
+
+    Profiles_list = use_case.generate_daily_load_profiles_parallel(flat=False)
     
     # Post-processes the results and generates plots
     Profiles_avg, Profiles_list_kW, Profiles_series = pp.Profile_formatting(Profiles_list)
     pp.Profile_series_plot(Profiles_series)  # by default, profiles are plotted as a series
     
-    if len(Profiles_list) > 1:  # if more than one daily profile is generated, also cloud plots are shown
+    if (
+        len(Profiles_list) > 1
+    ):  # if more than one daily profile is generated, also cloud plots are shown
         pp.Profile_cloud_plot(Profiles_list, Profiles_avg)
 
 
 .. parsed-literal::
 
-    Day 2022-01-01 00:00:00
-    Day 2022-01-02 00:00:00
-    Day 2022-01-03 00:00:00
-    Day 2022-01-04 00:00:00
-    Day 2022-01-05 00:00:00
-    Day 2022-01-06 00:00:00
-    Day 2022-01-07 00:00:00
-    Day 2022-01-08 00:00:00
-    Day 2022-01-09 00:00:00
+    Computing appliances profiles: 100%|████████| 90/90 [00:00<00:00, 1284.63unit/s]
 
 
 
-.. image:: output_5_1.png
+.. image:: output_8_1.png
 
 
 
-.. image:: output_5_2.png
+.. image:: output_8_2.png
 
 
-Run usecase with parallel processing
-------------------------------------
+You can also set the ``parallel_processing`` attribute of the
+``UseCase`` instance to ``True`` and use the
+``generate_daily_load_profiles``
 
 .. code:: ipython3
 
+    use_case.parallel_processing = True
     
-    day_types = [get_day_type(day) for day in days] 
-    
-    Profiles_list = uc.generate_daily_load_profiles_parallel(len(days), peak_time_range, day_types)
+    Profiles_list = use_case.generate_daily_load_profiles(flat=False)
     
     # Post-processes the results and generates plots
     Profiles_avg, Profiles_list_kW, Profiles_series = pp.Profile_formatting(Profiles_list)
     pp.Profile_series_plot(Profiles_series)  # by default, profiles are plotted as a series
     
-    if len(Profiles_list) > 1:  # if more than one daily profile is generated, also cloud plots are shown
+    if (
+        len(Profiles_list) > 1
+    ):  # if more than one daily profile is generated, also cloud plots are shown
         pp.Profile_cloud_plot(Profiles_list, Profiles_avg)
-    
-
 
 
 .. parsed-literal::
 
-    Computing appliances profiles: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 90/90 [00:00<00:00, 2093.11unit/s]
+    Computing appliances profiles: 100%|████████| 90/90 [00:00<00:00, 1437.07unit/s]
+
 
 
+.. image:: output_10_1.png
 
-.. image:: output_7_1.png
 
 
+.. image:: output_10_2.png
 
-.. image:: output_7_2.png
 
diff --git a/docs/source/examples/plot_class/output_38_1.png b/docs/source/examples/plot_class/output_38_1.png
index 1b40f54d..bca73826 100644
Binary files a/docs/source/examples/plot_class/output_38_1.png and b/docs/source/examples/plot_class/output_38_1.png differ
diff --git a/docs/source/examples/simple_bulb/output_6_1.png b/docs/source/examples/simple_bulb/output_6_1.png
index 2a668d5b..01f75a33 100644
Binary files a/docs/source/examples/simple_bulb/output_6_1.png and b/docs/source/examples/simple_bulb/output_6_1.png differ
diff --git a/docs/source/examples/simple_bulb/output_6_2.png b/docs/source/examples/simple_bulb/output_6_2.png
index 45ce748c..0e38050b 100644
Binary files a/docs/source/examples/simple_bulb/output_6_2.png and b/docs/source/examples/simple_bulb/output_6_2.png differ
diff --git a/docs/source/examples/simple_bulb/simple_bulb.rst b/docs/source/examples/simple_bulb/simple_bulb.rst
index 4b6666da..fd4ed059 100644
--- a/docs/source/examples/simple_bulb/simple_bulb.rst
+++ b/docs/source/examples/simple_bulb/simple_bulb.rst
@@ -1,10 +1,10 @@
-Simple Appliances with multiple functioning time
-================================================
+Simple Appliances with multiple functioning windows
+===================================================
 
 .. code:: ipython3
 
     # importing functions
-    from ramp import User,calc_peak_time_range,yearly_pattern
+    from ramp import User, UseCase, get_day_type
     import pandas as pd
 
 Creating a user category
@@ -13,8 +13,8 @@ Creating a user category
 .. code:: ipython3
 
     household = User(
-        user_name = "Household",
-        num_users = 10,
+        user_name="Household",
+        num_users=10,
     )
 
 Creating a simple appliance with two functioning time
@@ -23,25 +23,24 @@ Creating a simple appliance with two functioning time
 .. code:: ipython3
 
     indoor_bulb = household.add_appliance(
-        name = "Indoor Light Bulb",
-        number = 6,
-        power  = 7,
-        num_windows = 2,
-        func_time = 120,
-        time_fraction_random_variability = 0.2,
-        func_cycle = 10,
-        window_1 = [1170,1440], # from 19:30 to 24:00
-        window_2 = [0,30], # from 24 to 00:30
-        random_var_w = 0.35,
+        name="Indoor Light Bulb",
+        number=6,
+        power=7,
+        num_windows=2,
+        func_time=120,
+        time_fraction_random_variability=0.2,
+        func_cycle=10,
+        window_1=[1170, 1440],  # from 19:30 to 24:00
+        window_2=[0, 30],  # from 24 to 00:30
+        random_var_w=0.35,
     )
 
-
 .. code:: ipython3
 
     # Checking the maximum profile of the appliance and user
     
-    max_profile_bulb = pd.DataFrame(indoor_bulb.maximum_profile,columns=["appliance"])
-    max_profile_user = pd.DataFrame(household.maximum_profile,columns=["household"])
+    max_profile_bulb = pd.DataFrame(indoor_bulb.maximum_profile, columns=["appliance"])
+    max_profile_user = pd.DataFrame(household.maximum_profile, columns=["household"])
     
     max_profile_bulb.plot()
     max_profile_user.plot()
@@ -51,7 +50,7 @@ Creating a simple appliance with two functioning time
 
 .. parsed-literal::
 
-    <AxesSubplot:>
+    <Axes: >
 
 
 
@@ -63,128 +62,81 @@ Creating a simple appliance with two functioning time
 .. image:: output_6_2.png
 
 
-Defining the peak time range using the calc_peak_time_range
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Whole year profile functionality
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. code:: ipython3
 
-    peak_time_range = calc_peak_time_range(
-        user_list = [household]
-    )
-    
-    print(peak_time_range)
+    whole_year_profile = []
+    use_case = UseCase(users=[household], date_start="2020-01-01", date_end="2020-12-31")
+    whole_year_profile = use_case.generate_daily_load_profiles()
 
 
 .. parsed-literal::
 
-    [1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235
-     1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249
-     1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263
-     1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277
-     1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291
-     1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305
-     1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319
-     1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333
-     1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347
-     1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361
-     1362 1363 1364 1365 1366 1367 1368 1369]
+    You will simulate 366 day(s) from 2020-01-01 00:00:00 until 2021-01-01 00:00:00
 
 
-Defining the yearly patterns
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. code:: ipython3
 
-by default the yearly_pattern function returns weekdays (defined by 0)
-/weekends (defined by 1) division
+    whole_year_profile = pd.DataFrame(
+        whole_year_profile, columns=["household"], index=use_case.datetimeindex
+    )
+    whole_year_profile.plot()
 
-.. code:: ipython3
 
-    year_behaviour = yearly_pattern()
-    
-    print(year_behaviour)
 
 
 .. parsed-literal::
 
-    [0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0.
-     0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1.
-     1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0.
-     0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0.
-     1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0.
-     0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0.
-     0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1.
-     0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0.
-     0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1.
-     1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0.
-     0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0.
-     1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0.
-     0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0.
-     0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1.
-     0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0.
-     0. 0. 1. 1. 0.]
-
-
-Generating a profile for the ith day of the year
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    <Axes: >
 
-.. code:: ipython3
 
-    single_profile = household.generate_single_load_profile(
-        prof_i = 1, # the day to generate the profile
-        peak_time_range = peak_time_range,
-        Year_behaviour = year_behaviour
-    )
 
 
-.. code:: ipython3
+.. image:: output_9_1.png
 
-    single_profile = pd.DataFrame(single_profile,columns=["household"])
-    single_profile.plot()
 
+Generating a profile for a single day
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+provide day_type=0 for weekday and day_type=1 for weekends
 
+.. code:: ipython3
 
+    single_profile = household.generate_single_load_profile(day_type=0)
 
-.. parsed-literal::
+.. code:: ipython3
 
-    <AxesSubplot:>
+    single_profile = pd.DataFrame(single_profile, columns=["household"])
+    single_profile.plot()
 
 
 
 
-.. image:: output_13_1.png
+.. parsed-literal::
 
+    <Axes: >
 
-Whole year profile functionality
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. code:: ipython3
 
-    whole_year_profile = []
-    
-    for i in range(365):
-        whole_year_profile.extend(
-            household.generate_single_load_profile(
-                    prof_i = i,
-                    peak_time_range = peak_time_range,
-                    Year_behaviour = year_behaviour
-                    
-            )
-        )
 
-.. code:: ipython3
+.. image:: output_12_1.png
+
 
-    ### Generating aggregated_load_profile for the user category
+Generating aggregated_load_profile for the user category
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Single daily profiles are aggregated for all the users defined within
+the User class
 
 .. code:: ipython3
 
-    aggregated_profile = household.generate_aggregated_load_profile(
-        prof_i = 1,
-        peak_time_range = peak_time_range,
-        Year_behaviour = year_behaviour
-    )
+    aggregated_profile = household.generate_aggregated_load_profile()
 
 .. code:: ipython3
 
-    aggregated_profile = pd.DataFrame(aggregated_profile,columns = ["household"])
+    aggregated_profile = pd.DataFrame(aggregated_profile, columns=["household"])
     aggregated_profile.plot()
 
 
@@ -192,10 +144,11 @@ Whole year profile functionality
 
 .. parsed-literal::
 
-    <AxesSubplot:>
+    <Axes: >
+
 
 
 
+.. image:: output_15_1.png
 
-.. image:: output_18_1.png
 
diff --git a/docs/source/examples/thermal_app/output_15_1.png b/docs/source/examples/thermal_app/output_15_1.png
index db617f0a..e8d7662b 100644
Binary files a/docs/source/examples/thermal_app/output_15_1.png and b/docs/source/examples/thermal_app/output_15_1.png differ
diff --git a/docs/source/examples/thermal_app/output_6_1.png b/docs/source/examples/thermal_app/output_6_1.png
index 848383be..f13e7b8e 100644
Binary files a/docs/source/examples/thermal_app/output_6_1.png and b/docs/source/examples/thermal_app/output_6_1.png differ
diff --git a/docs/source/examples/thermal_app/thermal_app.rst b/docs/source/examples/thermal_app/thermal_app.rst
index 84fca84a..de36db0d 100644
--- a/docs/source/examples/thermal_app/thermal_app.rst
+++ b/docs/source/examples/thermal_app/thermal_app.rst
@@ -3,13 +3,17 @@ Thermal loads
 
 This example input file represents a single household user whose only
 load is the “shower”. The example showcases how to model thermal loads
-by means of the :code:`thermal_P_var` attribute.
+by: 1) using a time-varying average ``power`` attribute, pre-calculated
+as a function of the average daily groundwater temperature; and 2) using
+the ``thermal_p_var`` attribute to add further variability to the actual
+power absorbed by the appliance in each usage event, which reflects the
+randomness of user behaviour in preferring a slightly warmer or colder
+shower temperature.
 
 .. code:: ipython3
 
     # importing functions
-    from ramp import User,calc_peak_time_range,yearly_pattern
-    from ramp import load_data
+    from ramp import User, UseCase, load_data, get_day_type
     import pandas as pd
 
 Creating a user category and appliances
@@ -19,10 +23,11 @@ Creating a user category and appliances
 
     household = User()
 
-When the power is varying during the day, the “power” parameter needs to
-be passed as a pd.DataFrame or np.array with a daily profile (365 rows
-of data). For this exercise, data can be loaded from the default
-examples in ramp:
+When the power varies across days of the year, for instance, as a
+function of the average daily groundwater temperature, the “power”
+parameter can be passed as a ``pd.DataFrame`` or ``np.array`` with a
+daily profile (365 rows of data). For this exercise, data can be loaded
+from the default examples in ramp:
 
 .. code:: ipython3
 
@@ -38,7 +43,7 @@ examples in ramp:
 
 .. parsed-literal::
 
-    <AxesSubplot:>
+    <Axes: >
 
 
 
@@ -49,68 +54,55 @@ examples in ramp:
 .. code:: ipython3
 
     shower = household.add_appliance(
-        name = "Shower",
-        number = 1,
-        power  = shower_power,
-        num_windows = 2,
-        func_time = 15,
-        time_fraction_random_variability = 0.1,
-        func_cycle = 3,
-        window_1 = [390,540],
-        window_2 = [1080,1200],
-        random_var_w = 0.2
+        name="Shower",
+        number=1,
+        power=shower_power,
+        num_windows=2,
+        func_time=15,
+        time_fraction_random_variability=0.1,
+        func_cycle=3,
+        window_1=[390, 540],
+        window_2=[1080, 1200],
+        random_var_w=0.2,
     )
 
-
-Generating profiles
-~~~~~~~~~~~~~~~~~~~
+Generating profiles for increasing degrees of ``thermal_p_var``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. code:: ipython3
 
-    peak_time_range = calc_peak_time_range(
-        user_list = [household]
-    )
-    year_behaviour = yearly_pattern()
+    usecase = UseCase(users=[household], date_start="2020-01-01")
+    usecase.initialize(num_days=365)
 
-.. code:: ipython3
 
-    # generating the yearly profiles for different thermal_P_var
+.. parsed-literal::
+
+    You will simulate 365 day(s) from 2020-01-01 00:00:00 until 2020-12-31 00:00:00
+
 
 .. code:: ipython3
 
-    def thermal_P_var_sensitivity(values):
-        
+    def thermal_p_var_sensitivity(values):
         # buidling a pd.DataFrame for saving sensitivity results
-        results = pd.DataFrame(    
-            index = pd.date_range(start = "2020-01-01",periods = 1440*365,freq="T"),
-            columns = [f"P_var = {value}" for value in values]
+        results = pd.DataFrame(
+            index=pd.date_range(start="2020-01-01", periods=1440 * 365, freq="T"),
+            columns=[f"p_var = {value}" for value in values],
         )
-        
+    
         for value in values:
-            
             # changing the thermal_P_var
-            household.thermal_P_var = value
-            
-            # creating a list to sotre profiles for all the years of the year
-            profiles = []
-            for day in range(365):
+            shower.thermal_p_var = value
+    
+            profiles = usecase.generate_daily_load_profiles(flat=True)
     
-                profile = household.generate_single_load_profile(
-                        prof_i = day, 
-                        peak_time_range = peak_time_range,
-                        Year_behaviour = year_behaviour
-                    )
+            # assigning the yearly profile for a given sensitivity case
+            results[f"p_var = {value}"] = profiles
     
-                profiles.extend(profile)
-                
-            # assiging the yearly profile for a given sensitivity case
-            results[f"P_var = {value}"] = profiles
-            
         return results
 
 .. code:: ipython3
 
-    sensitivity_results = thermal_P_var_sensitivity([0,0.25,0.5,0.75,1])
+    sensitivity_results = thermal_p_var_sensitivity([0, 0.25, 0.5, 0.75, 1])
 
 .. code:: ipython3
 
@@ -139,11 +131,11 @@ Generating profiles
       <thead>
         <tr style="text-align: right;">
           <th></th>
-          <th>P_var = 0</th>
-          <th>P_var = 0.25</th>
-          <th>P_var = 0.5</th>
-          <th>P_var = 0.75</th>
-          <th>P_var = 1</th>
+          <th>p_var = 0</th>
+          <th>p_var = 0.25</th>
+          <th>p_var = 0.5</th>
+          <th>p_var = 0.75</th>
+          <th>p_var = 1</th>
         </tr>
       </thead>
       <tbody>
@@ -256,10 +248,31 @@ Generating profiles
 
 .. parsed-literal::
 
-    <AxesSubplot:>
+    <Axes: >
+
+
+
+
+.. image:: output_14_1.png
+
+
+.. code:: ipython3
+
+    first_day = pd.date_range(
+        start="2020-01-01 00:00:00", freq="1min", periods=24 * 60  # a full day
+    )
+    sensitivity_results.loc[first_day].plot()
+
+
+
+
+.. parsed-literal::
+
+    <Axes: >
 
 
 
 
 .. image:: output_15_1.png
 
+
diff --git a/docs/source/examples/using_excel/output_17_1.png b/docs/source/examples/using_excel/output_17_1.png
index 6f66029f..f518803b 100644
Binary files a/docs/source/examples/using_excel/output_17_1.png and b/docs/source/examples/using_excel/output_17_1.png differ
diff --git a/docs/source/examples/using_excel/using_excel.rst b/docs/source/examples/using_excel/using_excel.rst
index 25209c9b..a97dba6a 100644
--- a/docs/source/examples/using_excel/using_excel.rst
+++ b/docs/source/examples/using_excel/using_excel.rst
@@ -1,32 +1,31 @@
 Using tabular inputs to build a model
-============================
+=====================================
 
-When the number of users or appliances is high, it can be difficult 
-to create a model using python scripts. Therefore, RAMP allows you 
-to create inputs in tabular format (.xlsx). In this example we show
-a use case of this functionality.
+When the number of users or appliances is high, it can be difficult to
+create a model using Python scripts. Therefore, RAMP allows you to
+create inputs in tabular format (``.xlsx``). On the other hand, it is
+still possible to use Python to generate a large tabular file with
+default parameter values, which can then be more easily customised. In
+this example, we show a possible utilisation of this functionality.
 
 .. code:: ipython3
 
-    from ramp import User, Appliance, UseCase
-    from ramp import calc_peak_time_range,yearly_pattern
+    from ramp import User, Appliance, UseCase, get_day_type
     import pandas as pd
 
-At the first step, user needs to creat User classes and assign
-Appliances to the user class without assiging any detailed appliance
-characteristics
+As a first step, one must create ``User`` classes and assign
+``Appliances`` to the user class without assigning detailed appliance
+characteristics. Hence, users and their appliances are added to a
+``UseCase``.
 
-Buidling a model database
-~~~~~~~~~~~~~~~~~~~~~~~~~
+Building a tabular file populated with default data
+---------------------------------------------------
 
 .. code:: ipython3
 
     # Defining a dict of users with their appliances
     
-    user_app = {
-        "household" : ["light","tv"],
-        "school": ["light","computer"]
-    }
+    user_app = {"household": ["light", "tv"], "school": ["light", "computer"]}
 
 .. code:: ipython3
 
@@ -36,26 +35,25 @@ Buidling a model database
 .. code:: ipython3
 
     # assinging the appliances to users
-    for user,apps in user_app.items():
-        
-        user_instance = User(user_name = user)
-        
+    for user, apps in user_app.items():
+        user_instance = User(user_name=user)
+    
         for app in apps:
             app_instance = user_instance.add_appliance(name=app)
             app_instance.windows()
-            
+    
         use_case.add_user(user_instance)
 
 
 .. parsed-literal::
 
-    UserWarning: No windows is declared, default window of 24 hours is selected
-      warnings.warn(UserWarning("No windows is declared, default window of 24 hours is selected"))
+    /home/fl/GitHub-repos/RAMP/ramp/core/core.py:1198: UserWarning: No windows is declared, default window of 24 hours is selected
+      warnings.warn(
 
 
-Once the users and appliances are added to the :code:`use_case` instance, the
-user can get a pd.DataFrame or an .xlsx file of all the data with the
-default values.
+Once the ``Users`` and ``Appliances`` are added to the ``use_case``
+instance, the model user can get a ``pd.DataFrame`` or an ``.xlsx`` file
+of all the data with the default values.
 
 Exporting the database
 ~~~~~~~~~~~~~~~~~~~~~~
@@ -218,11 +216,12 @@ Exporting the database
 .. code:: ipython3
 
     # Printing out the database to an .xlsx file
-    use_case.save("path/name_of_file")
+    use_case.save("example_excel_usecase")
 
-Once the function is used, an .xlsx file will be created in the given
-path. Now you can easily fill-out the information in the .xlsx file and
-load the data into the model database as detailed below.
+Once the function is used, an ``.xlsx`` file will be created in the
+given path. Now, you can easily fill out the information in the
+``.xlsx`` file and load the data into the model database as detailed
+below.
 
 Loading the database
 ~~~~~~~~~~~~~~~~~~~~
@@ -231,54 +230,88 @@ Loading the database
 
     # loading data
     
-    use_case = UseCase() # creating a new UseCase instance
-    use_case.load("path/name_of_file.xlsx")
+    use_case = UseCase()  # creating a new UseCase instance
+    use_case.load("example_excel_usecase_filled.xlsx")
 
 Generating load profiles
-~~~~~~~~~~~~~~~~~~~~~~~~
+------------------------
 
 Once the database is loaded, the user can continue with the normal
-analysis like generating aggregated profiles
+analysis, for instance, generating aggregated profiles
+
+.. code:: ipython3
+
+    n_days = 30
+    date_start = "2020-01-01"
+    use_case.date_start = date_start
+    use_case.initialize(num_days=n_days, force=True)
+    use_case.generate_daily_load_profiles()
+
+
+.. parsed-literal::
+
+    You will simulate 30 day(s) from 2020-01-01 00:00:00 until 2020-01-31 00:00:00
+
+
+
+
+.. parsed-literal::
+
+    array([0.   , 0.   , 0.   , ..., 0.002, 0.002, 0.002])
+
+
 
 .. code:: ipython3
 
-    peak_time_range = calc_peak_time_range(
-        user_list = use_case.users
+    profiles = pd.DataFrame(
+        data=use_case.generate_daily_load_profiles(flat=True),
+        index=pd.date_range(start=date_start, periods=1440 * n_days, freq="T"),
     )
     
-    year_behaviour = yearly_pattern()
+    profiles.plot(title="Usecase")
+
+
+
+
+.. parsed-literal::
+
+    <Axes: title={'center': 'Usecase'}>
+
+
+
+
+.. image:: output_17_1.png
+
+
+Generating load profiles for the single users of the usecase
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. code:: ipython3
 
-    n_days = 30
-    
-    
-    
     for user in use_case.users:
         user_profiles = []
-        for day in range(n_days):
+        for day_idx, day in enumerate(use_case.days):
             profile = user.generate_aggregated_load_profile(
-                            prof_i = day,
-                            peak_time_range = peak_time_range,
-                            Year_behaviour = year_behaviour
-                        )
-            
+                prof_i=day_idx,
+                peak_time_range=use_case.peak_time_range,
+                day_type=get_day_type(day),
+            )
+    
             user_profiles.extend(profile)
-            
-        profiles = pd.DataFrame(   
-            data = user_profiles,
-            index = pd.date_range(start = "2020-01-01",periods = 1440*n_days,freq="T"),
+    
+        profiles = pd.DataFrame(
+            data=user_profiles,
+            index=pd.date_range(start=date_start, periods=1440 * n_days, freq="T"),
         )
-        
-        profiles.plot(title = user.user_name)
-        
-            
+    
+        profiles.plot(title=user.user_name)
 
 
 
-.. image:: output_17_0.png
+.. image:: output_19_0.png
 
 
 
-.. image:: output_17_1.png
+.. image:: output_19_1.png
+