Model updated
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26th July 2024
Summary
The retraining process resulted in two new models, one for PD and one for 7day price forecast. After experimentation we retrained the price forecasting models using existing features and hyper parameter settings. The increase in accuracy (using Absolute Mean Error, AME) was approximately 5%. (see validation results table below comparing the previous models with the new models).
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The model was validated by predicting the price between 09-07-2024 and 2123-07-2024.
New features (input data) exceptions
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Predispatch model | p7day model | |
|---|---|---|
Model_id | 20240503_090016_CB_2 | 20240503_085256_CB_7D_2 |
Latest release date (after retraining) | 26th July 2024 | 26th July 2024 |
Previous model release date | 26th July April 2024 | 26th July April 2024 |
Machine Learning Algorithm | Ensemble predictor: gradient boosting | Ensemble predictor: gradient boosting |
Training Period | 1st July 2021 to 8th July 2024 | 1st July 2021 to 8th July 2024 |
Hyper-parameter type | A | A |
Hyper-parameter notes | No weighting across time periods | No weighting across time periods |
Target | Dispatchprice (30min resolution) | Dispatchprice (30min resolution) |
Input (and training) variables | PREDISPATCHPRICE rrp DISPATCHPRICE last 3 day average actual RRP for same time PREDISPATCHREGIONSUM (intervention = 0) hours_out time_of_day day_type (weekday_or_weekend) dispatchableload netinterchange (availablegeneration - uigf) (dispatchablegeneration - scheduled_clearedmw) ss_solar_clearedmw ss_solar_uigf ss_wind_clearedmw ss_wind_uigf totaldemand totalintermittentgeneration | STPASA_REGIONSOLUTION hours_out time_of_day day_type (weekday_or_weekend) aggregatepasaavailability aggregatescheduledload netinterchangeunderscarcity aggregatecapacityavailable ss_solar_cleared ss_wind_uigf ss_wind_cleared ss_solar_uigf demand10 demand50 totalintermittentgeneration demand_and_nonschedgen |
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