Cooking Appliances
==================

Some appliances load profiles, highly depend on the user choices and
preferences. For example, electric stoves power usage, highly depends on
the type of food and that a user wants to cook. This kind of appliances
in RAMP are flagged by the user category consumption preferences.

In this example, we will see how the electric cookstoves with multiple
user preferences can be modelled in RAMP.

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 every day.
2. A household with two lunch preferences: cooking soup or rice.

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
    import pandas as pd
    import matplotlib.pyplot as plt

Creating a user category
~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: ipython3

    user_1 = User(
        user_name="Household with single lunch habit",
        num_users=1,
        user_preference=1,  # user_1 has only one consumption preference
    )
    
    user_2 = User(
        user_name="Household with different lunch habits",
        num_users=1,
        user_preference=2,  # user_2 has two consumption preferences
    )

Defining the cycles for cooking soup and rice
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

For cookstoves, it will be more realistic to have different operation
cycles as cooking a food usually requires different levels of heat for
the different parts of food processing:

For cooking soup it is assumed that the user needs 25 minutes divided
into two parts:

============= ===== ====
cycle         power time
============= ===== ====
Boiling Water 1200  5
Cooking soup  750   20
============= ===== ====

For cooking rice it is assumed that the user needs 15 minutes divided
into two parts:

============= ===== ====
cycle         power time
============= ===== ====
Boiling Water 1200  5
Cooking rice  600   10
============= ===== ====

.. code:: ipython3

    # soup for lunch
    lunch_window = [12 * 60, 12 * 60 + 26]
    
    soup_1 = user_1.add_appliance(
        name="soup for lunch",
        power=1200,  # nominal power of appliance
        func_time=25,  # the cooking time
        func_cycle=25,  # we always need  25 minute for cooking
        fixed_cycle=1,  # the cookstove is not a continus power usage appliance, it has cycles as mentioned earlier
        window_1=lunch_window,  # lunch is always prepared from 12
        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=lunch_window,  # the time window of the working cycle
    )

The second user has two different preferences for lunch. Accordingly, we
need to model these preferences and their characteristics 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.

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).

.. code:: ipython3

    # soup for lunch
    soup_2 = user_2.add_appliance(
        name="soup for lunch",
        power=1200,
        func_time=25,
        func_cycle=25,
        fixed_cycle=1,
        window_1=lunch_window,
        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=lunch_window,
        pref_index=1,  # the first preference
    )

.. code:: ipython3

    # rice for lunch
    rice_2 = user_2.add_appliance(
        name="rice for lunch",
        power=1200,
        func_time=15,
        func_cycle=15,
        fixed_cycle=1,
        window_1=lunch_window,
        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=lunch_window,
        pref_index=2,  # the second preference
    )

.. code:: ipython3

    number_of_days = 5
    user_1_profiles = {}
    user_2_profiles = {}
    for day in range(1, number_of_days + 1):
        user_1_profiles[f"day {day}"] = pd.Series(user_1.generate_single_load_profile())
        user_2_profiles[f"day {day}"] = pd.Series(user_2.generate_single_load_profile())


.. parsed-literal::

    You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you 
    You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you 


.. code:: ipython3

    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))
    
    
    i = 0
    for name, df in dict(
        user_1_profiles=pd.concat(user_1_profiles, axis=1).iloc[
            lunch_window[0] - 5 : lunch_window[1] + 5
        ],  # take only the lunch window
        user_2_profiles=pd.concat(user_2_profiles, axis=1).iloc[
            lunch_window[0] - 5 : lunch_window[1] + 5
        ],  # take only the lunch window
    ).items():
        df.plot(ax=axes[i], legend=True)
        axes[i].set_title(name)
        i += 1
    
    plt.tight_layout()
    plt.show()



.. image:: output_10_0.png


As it can be observed, user_1 always have the same demand profile for
lunch prepration while user_2 can have two different profiles (for
example on day 3 and 4, the user cooks rice for lunch)!

:download:`Link to the jupyter notebook file </../notebooks/cooking_app.ipynb>`.