Thermal loads
=============

Some of the energy requirements of users are drived my technologies that
have time variant power consumption. Hot water boilers are an example of
technologies that can have variant powers throughout time. To show case
how loads as such can be modelled in RAMP, we take the example of a
household thermal load for generating shower hot water.

.. code:: ipython3

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

Creating a user category and appliances
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: ipython3

    household = User()

RAMP allows the user to give variant power profile for an appliance with
a daily resolution. In this case,the power property in an appliance
should be given as a timeseries pd.DataFrame or np.array with daily
profile (365 rows of data). For this exercise, we will use a built-in
power profile for thermal load example that can be loaded using
load_data util function:

.. code:: ipython3

    shower_power = load_data(example="shower")
    # shower power distribution
    shower_power.plot()




.. parsed-literal::

    <Axes: >




.. image:: output_5_1.png


.. code:: ipython3

    shower = household.add_appliance(
        name="Shower",
        number=1,
        power=shower_power,  # pass the pd.DataFrame or np.array instead of a number
        num_windows=2,  # two possibe time window for shower
        func_time=15,  # each shower takes 15 minute
        func_cycle=3,  # every
        window_1=[390, 540],  #  morning shower from 6:30 to 9:00 AM
        window_2=[1080, 1200],  # evening shower from 18:00 to 20:00
    )

Generating profiles for increasing degrees of ``thermal_p_var``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: ipython3

    usecase = UseCase(users=[household], date_start="2020-01-01")
    usecase.initialize(num_days=365)


.. parsed-literal::

    You will simulate 365 day(s) from 2020-01-01 00:00:00 until 2020-12-31 00:00:00


.. parsed-literal::

    c:\users\tahavorm\downloads\gitrepos\ramp\ramp\core\core.py:299: FutureWarning: 'T' is deprecated and will be removed in a future version. Please use 'min' instead of 'T'.
      end=self.days[-1] + pd.Timedelta(1, "d") - pd.Timedelta(1, "T"),
    c:\users\tahavorm\downloads\gitrepos\ramp\ramp\core\core.py:297: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.
      self.__datetimeindex = pd.date_range(


As everyone has a different habit in the water temperature for taking
shower, we can also consider a variability in the thermal power through
the thermal_p_var property. 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.To better
understand the effect of this parameters, let’s perform a sensitivity
analysis on themal_p_var function. To do so, we use the following
function **thermal_p_var_sensitivty**:

.. code:: ipython3

    def thermal_p_var_sensitivity(sensitivity_values):
        # buidling a pd.DataFrame for saving sensitivity results
        results = pd.DataFrame(
            index=range(0, 1440 * 365),
            columns=[f"p_var = {value}" for value in sensitivity_values],
        )
    
        for value in sensitivity_values:
    
            # changing the thermal_P_var
            shower.thermal_p_var = value
    
            profiles = usecase.generate_daily_load_profiles()
    
            # assigning the yearly profile for a given sensitivity case
            results[f"p_var = {value}"] = profiles
    
        return results

.. code:: ipython3

    # generating 5 senstivities on thermal_p_var
    sensitivity_results = thermal_p_var_sensitivity([0, 0.25, 0.5])

.. code:: ipython3

    days_to_plot = [1, 100, 200]  # which days of the year to plot
    
    fig, axes = plt.subplots(
        ncols=len(days_to_plot), nrows=sensitivity_results.shape[1], figsize=(10, 10)
    )
    
    for j, day in enumerate(days_to_plot):
        for i, col in enumerate(sensitivity_results):
            sensitivity_results[col].iloc[1440 * (day - 1) : 1440 * (day)].plot(
                ax=axes[i, j]
            )  # just plot for the first day
            axes[i, j].set_title(f"Day {day} - thermal_p_var = {col}", fontsize=8)
            axes[i, j].set_ylim(0, 24000)
    
    
    plt.tight_layout()
    plt.show()



.. image:: output_12_0.png


As it can be observed, the power consumption of hot water supply
technology varies across different days of the year, primarily due to
fluctuations in the nominal power. When adjusting the parameter
**thermal_p_var** from 0, indicating no variability in power consumption
due to user preferences, to higher values, which signify the probability
of changes in hot water temperature, the power consumption also varies
accordingly.

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