Model updated
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28th January 2025
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 between 10% and 15% (with the exception of SA which was 50%)main benefit of the retrained model is that it includes the most recent market outcomes which will therefore be factored into the model. (see validation results table below comparing the previous models with the new models).
Training period
Both PD and 7day price forecast models were trained on data between 01-
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01-
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2022 and 01-
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24-
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2025.
The market suspension period and a period after the Callide explosion were removed from the training data.
Validation set
This model was not validated, rather we felt that it was better to train the model up to the most recent time period. Earlier models have maintained the feature set and hyper parameter settings.
New features (input data) exceptions
The feature set is the same as the previous model. The main benefit of the new model is that more recent market data are consumed in the training process. The training does not weight the most recent market data.
A number of experiments were conducted by changing the feature list and some hyper parameters however this didn’t not achieve a significant benefit to the model tests. Hence for consistency we have not made changes to the model other than extending the training period to the most recent market data. Unlike the August model update included significant changes to both the feature set and the hyper parameters.
Predispatch model | p7day model | ||||||
|---|---|---|---|---|---|---|---|
Model_id | 2023110820250121_142510160406_CB_2 | 2023110820250121_140809160406_CB_7D_2 | |||||
Previous model Latest release date | 8th August 2023 | 8th August 2023 | Latest release date (after retraining) | 9th November 2023 | 9th November 2023(after retraining) | 28th January 2025 | 28th January 2025 |
Previous model release date | 12th November 2024 | 12th November 2024 | |||||
Machine Learning Algorithm | Ensemble predictor: gradient boosting | Ensemble predictor: gradient boosting | |||||
Training Period | 1st October 2020 to 31st October 20231st October 2020 to 31st October 2023January 2022 to 24th January 2025 | 1st January 2022 to 24th January 2025 | |||||
Validation Period | N/A | N/A | |||||
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 netinterchagenetinterchange (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 |
Validation Results
These are the results of the best model generated through our experimentation process and compares the results with the previous model across our validation set (our test period)Previous experimentation demonstrates that the current model features and settings provides a pretty good model across most market conditions. Therefore a validation set was not used rather the new model was trained right up to the latest market outcomes.
Average error is equal to Predication minus real prices.
All error metrics are in $/MWhr.
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PD Results
Metric/ Region | Actual Mean Price | Mean Prediction New Model | Mean Prediction Previous Model | Absolute Mean Error New Model | Absolute Mean Error Previous Model | Average Error New Model | Average Error Previous Model | |||
|---|---|---|---|---|---|---|---|---|---|---|
NSW1 | 37.16954 | 63.55691 | 19.63544 | 38.2957 | ||||||
VIC1 | 29.91769 | 37.21212 | 6.468355 | 2.998254 | ||||||
QLD1 | 36.46522 | 132.3778 | 15.79482 | 112.1593 | ||||||
SA1 | 45.14167 | 65.13762 | 19.56986 | 35.5676 | ||||||
TAS1 | 19.53866 | 17.71054 | 9.396361 | 6.877853124.56 | 146.64 | 148.64 | 41.51 | 44.01 | 22.08 | 24.08 |
QLD1 | 111.98 | 135.52 | 138.38 | 49.09 | 52.39 | 23.54 | 26.41 | |||
SA1 | 178.34 | 172.26 | 177.12 | 92.05 | 93.88 | -6.07 | -1.21 | |||
TAS1 | 196.72 | 151.68 | 151.90 | 62.24 | 63.75 | -45.04 | -44.82 | |||
VIC1 | 159.29 | 144.44 | 146.75 | 41.98 | 42.36 | -14.85 | -12.55 |
7 Day Results
Metric/ Region | Actual Mean Price | Mean Prediction New Model | Mean Prediction Previous Model | Absolute Mean Error New Model | Absolute Mean Error Previous Model | Average Error New Model | Average Error | Previous Previous Model |
|---|---|---|---|---|---|---|---|---|
NSW1 | 34124.0643133 | 48149.1010482 | 15157.0169316 | 3725.0869349 | VIC132.84 | 3158.6689199 | 4460.8371547 | |
QLD1 | 1110.21520600 | 19124.2093643 | QLD1143.61 | 3914.1001443 | 5333.9197661 | 1766.3268291 | 70.35.32128 | |
SA1 | 45.46252 | 104.0136 | 21.62486 | 78.54663 | ||||
TAS1 | 24.07958 | 45.70449 | 14.92562 | 39.71673 | ||||
SA1 | 187.49 | 423.72 | 410.66 | 236.23 | 223.17 | 304.11 | 317.39 | |
TAS1 | 198.77 | 111.08 | 112.95 | -87.69 | -85.82 | 93.36 | 95.67 | |
VIC1 | 161.41 | 158.90 | 161.17 | -2.51 | -0.24 | 61.76 | 64.03 |
| Info |
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Variables that imply binding interconnectors (such as limits and marginal values) are currently not included as features. |
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