Open dmuldrew opened 1 year ago
Currently our LDV immediate test does not use adjustment_values in the following function in immediate.py:
adjustment_values
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?
I will look into this.
Currently our LDV immediate test does not use
adjustment_values
in the following function inimmediate.py
:We need to define a strategy for calculating this parameter which incorporates urban and rural scaling?