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).

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:

+--------+------------------------------+--------------+--------------+
| season | Standard cycle               | Intermediate | Intensive    |
| type   |                              | cycle        | cycle        |
+========+==============================+==============+==============+
| Hot    | 00:00:00 - 04:59:00 &        | 05:00:00 -   | 08:00:00 -   |
|        | 20:01:00 - 23:59:00          | 07:59:00     | 20:00:00     |
+--------+------------------------------+--------------+--------------+
| Warm   | 00:00:00 - 04:59:00 &        | 05:00:00 -   | 09:40:00 -   |
|        | 18:01:00 - 23:59:00          | 09:39:00     | 18:00:00     |
+--------+------------------------------+--------------+--------------+
| Cold   | 00:00:00 - 04:59:00 &        | 05:00:00 -   | -            |
|        | 20:01:00 - 23:59:00          | 20:00:00     |              |
+--------+------------------------------+--------------+--------------+

Creating the user and appliance
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: ipython3

    # importing functions
    from ramp import User, UseCase, get_day_type
    import pandas as pd

.. code:: ipython3

    # creating user
    household = User()

.. code:: ipython3

    # 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
    )

.. code:: ipython3

    # setting the functioning windows
    fridge.windows([0, 1440])  # always on during the whole year

Assigining the specific cycles
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: ipython3

    # assiging the specific cycles
    # first cycle: standard cycle
    fridge.specific_cycle_1(
        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,
    )
    
    # third cycle: intensive cycle
    fridge.specific_cycle_3(
        p_31=200,
        t_31=10,
        p_32=5,
        t_32=20,
    )

After defining the cycle power and duration parameters, the time windows
of year at which the cycles happens should be specifid by:

.. code:: ipython3

    # defining cycle behaviour
    fridge.cycle_behaviour(
        cw11=[480, 1200], cw21=[300, 479], cw31=[0, 229], cw32=[1201, 1440]
    )

Buidling the profiles
~~~~~~~~~~~~~~~~~~~~~

.. code:: ipython3

    use_case = UseCase(users=[household])
    peak_time_range = use_case.calc_peak_time_range()

.. code:: ipython3

    # days to build the profiles
    days = [
        "2020-05-16",
        "2020-08-16",
        "2020-12-16",
    ]
    
    profiles = pd.DataFrame(index=range(0, 1440), columns=days)
    
    for day_idx, day in enumerate(days):
        profile = household.generate_single_load_profile(
            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

    print(profiles)


.. parsed-literal::

          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]


.. code:: ipython3

    # plotting a part of the days
    profiles.iloc[400:500].plot()




.. parsed-literal::

    <AxesSubplot:>




.. image:: output_15_1.png