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# Forecastor for Buildings’ Consumption

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### Installation Command line: pip install building_energy_forecastor

### Features’ list for preprocessing data from src/building_preprocess * day_of_week(date_serie): Takes a pandas.Series of dates and returns a pandas.Series of corresponding week days ([‘Monday’, ‘Tuesday’, …]). * set_time_index(df, timeindex=’Timestamp’): Set the time column as index of the dataframe df. By default the column’s label is ‘Timestamp’. * time_to_cycle(df, timeindex=’Timestamp’): From the 3rd competitor of the [Forecast challenge](https://www.drivendata.org/competitions/51/electricity-prediction-machine-learning/) by Schneider Electric. Add column to a copy of df containing cosinus and sinus functions of the time of the day, the month of the year and the day of the year. * add_weather(df, weather, timeindex=’Timestamp’, freq_temp=’D’): From the 3rd competitor of the [Forecast challenge](https://www.drivendata.org/competitions/51/electricity-prediction-machine-learning/) by Schneider Electric. Adds the weather data to the training dataset (df here) merging the two dataframes on the ‘Timestamp’ and rouding the time value in weather to the precised freq_temp (‘D’ by default). * fill_temperature(df, tempindex=’Temperature’): fill the NaN values in the tempindex column by computing the mean on the two closest framing values.

### Model functions from src/building_model * building_regressor(): Returns a linear regressor from Scikit-learn. * building_train(reg, X, y): Trains the regressor with X the data and Y the targeted values. * building_prediction(reg, X): Returns a pandas.DataFrame showing the prediction of the regressor reg given the data X.

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