Appliances with occasional use
==============================

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**.

When ``occasional_use = 1``, the appliance is always present in the mix,
and when ``occasional_use = 0``, 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 categories: \* A household that uses a computer
occasionally \* A school that uses the computer every day

.. code:: ipython3

    # importing functions
    from ramp import User, UseCase
    import matplotlib.pyplot as plt
    import pandas as pd

Creating user categories and appliances
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: ipython3

    household = User("Household")
    school = User("School")

.. code:: ipython3

    computer_0 = household.add_appliance(
        name="Household Computer",
        number=1,
        power=50,
        num_windows=1,
        func_time=210,  # 3.5 hours
        func_cycle=210,
        occasional_use=0.5,  # 50% chance of occasional use,
        window_1=[480, 750],  # start from 8AM
    )

.. code:: ipython3

    computer_1 = school.add_appliance(
        name="School Computer",
        number=1,
        power=50,
        num_windows=1,
        func_time=210,  # 3.5 hours
        func_cycle=210,
        occasional_use=1,  # always present in the mix of appliances,
        window_1=[480, 750],  # start from 8AM
    )

Generating profiles
~~~~~~~~~~~~~~~~~~~

As the profiles of each specific User category is important, we will use
the User object profile genertor methods for 5 consecutive days:

.. code:: ipython3

    number_of_days = 5
    household_profiles = []
    school_profiles = []
    
    for day in range(1, number_of_days + 1):
        household_profiles.extend(household.generate_single_load_profile(prof_i=day))
    
        school_profiles.extend(school.generate_single_load_profile(prof_i=day))


.. 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(
        household_profiles=pd.DataFrame(household_profiles),
        school_profiles=pd.DataFrame(school_profiles),
    ).items():
        df.plot(ax=axes[i], legend=False)
        axes[i].set_title(name)
        i += 1
    
    plt.tight_layout()
    plt.show()



.. image:: output_8_0.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.



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