immediate charging: calculate adjustment values strategy

[dmuldrew]

Currently our LDV immediate test does not use adjustment_values in the following function in immediate.py:

def adjust_bev(hourly_profile, adjustment_values):  # noqa: N802
    """Adjusts the charging profiles by applying weighting factors based on
    seasonal/monthly values

    :param numpy.ndarray hourly_profile: normalized charging profiles
    :param pandas.DataFrame adjustment_values: weighting factors for each
        day of the year loaded from month_info_nhts.mat.
    :return: (*numpy.ndarray*) -- the final adjusted charging profiles.
    """
    adj_vals = adjustment_values.transpose()
    profiles = hourly_profile.reshape((24, 365), order="F")

    pr = profiles / sum(profiles)
    adjusted = pr * adj_vals

    return adjusted.T.flatten()

We need to define a strategy for calculating this parameter which incorporates urban and rural scaling?

[keforres]

I will look into this.