Examples of ClassModel

Here an example of how we cloud use the Dos’s package:

The Impodtation of the Package:

>>> from Project.Prediction import ClassModel as md
>>> from Project.Prediction import DataCollection as dc
>>> import pandas as pd

Load the data

>>> df = dc.Data()
>>> df = df.impo() # data from 2019-01-01 00:00:00 to 2022-11-14 23:45:00
>>> df.head(10)
                    Time  Consommation (MW)  Gaz (MW)  Nucléaire (MW)
    0  2019-01-01 00:00:00            64207.0    3430.0         55577.0
    1  2019-01-01 00:15:00            63684.5    3229.5         55894.0
    2  2019-01-01 00:30:00            63162.0    3029.0         56211.0
    3  2019-01-01 00:45:00            62042.5    2943.5         55625.0
    4  2019-01-01 01:00:00            60923.0    2858.0         55039.0
    5  2019-01-01 01:15:00            60826.0    2862.0         55154.0
    6  2019-01-01 01:30:00            60729.0    2866.0         55269.0
    7  2019-01-01 01:45:00            60428.0    2845.5         55109.5
    8  2019-01-01 02:00:00            60127.0    2825.0         54950.0
    9  2019-01-01 02:15:00            59786.5    2828.5         54998.5

set Time as index:

>>> df.set_index("Time", inplace = True)
>>> df.index = pd.to_datetime(df.index)
>>> df.tail(5)
                        Consommation (MW)  Gaz (MW)  Nucléaire (MW)
    Time
    2022-12-06 17:45:00            70553.0    8359.0         36545.0
    2022-12-06 18:00:00            71257.0    8350.0         36543.0
    2022-12-06 18:15:00            71685.0    8229.0         36522.0
    2022-12-06 18:30:00            72746.0    8248.0         36495.0
    2022-12-06 18:45:00            72746.0    8318.0         36491.0

Calling the Dos class and creating featurs by calling the createFeatures() method, setting 0 as parametres to mention to Electricity Consommation, 1 for Gaz and 2 for Nuclear

::
>>> Model = md.Dos(df, 0, 2022, 12, 8)
>>> Featurs = Model.createFeatures()
>>> Featurs.head(4)

Consommation (MW) Gaz (MW) Nucléaire (MW) minute … dayofmonth lag1 lag2 lag3

Time … 2022-12-06 18:00:00 71257.0 8350.0 36543.0 0 … 6 76880.0 47161.0 64641.0 2022-12-06 18:15:00 71685.0 8229.0 36522.0 15 … 6 77336.0 48289.0 65521.0 2022-12-06 18:30:00 72746.0 8248.0 36495.0 30 … 6 77792.0 48745.0 66010.0 2022-12-06 18:45:00 72746.0 8318.0 36491.0 45 … 6 78373.5 49813.0 66808.0

[4 rows x 12 columns]

Fiting the model by calling the class fitModel() and prediction of 8 decembre

>>> reg = Model.fitModel()
>>> dayPred, date = Model.DayPred(reg)
>>> dayPred
                 Date  Heure  Consommation (MW)
    0  2022-12-08  00:00       63028.617188
    1  2022-12-08  00:15       62377.496094
    2  2022-12-08  00:30       60382.480469
    3  2022-12-08  00:45       59399.277344
    4  2022-12-08  01:00       58877.019531

Last thing is to call Plot method by using this command.

::
>>> Model.plot(dayPred,date)
Chart