forecast.py

## Build Features for forecasting

build_forecast_df = build_features_forecast(df.energy, features_request, include_tzero=True)
scaled_features_forecast = scaler_features.transform(build_forecast_df.tail(48))
pred = model.predict(scaled_features_forecast[:, :-1])
pred = scaler_label.inverse_transform(pred)

# Range for next 24 hours
range = pd.date_range(start=build_df.index[-1]+timedelta(minutes=30), 
              end=build_df.index[-1]+timedelta(hours=24), 
              freq="30T")
range.freq = None
range = range.to_list()


# Request
### Get actual values
new_data = pvl.between(pytz.utc.localize(range[0]), pytz.utc.localize(range[-1]))
new_data = np.array([x[2] for x in new_data])