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This page is no longer being updated and will be replaced when Second Tier pd4castr subscription is released that will include multiple models.

Model updated 5th February 2024

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  • 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.

  • Validation set is from the 1st of November to the 7th of November. Oddly enough the impact of the Melbourne Cup did not have a significant impact on the performance metrics.

 

 

Predispatch model

p7day model

Model_id

20231108_142510_CB_2

20231108_140809_CB_7D_2

Previous model release date

9th November 2023

9th November 2023

Latest release date (after retraining)

5th February 2024

5th February 2024

Machine Learning Algorithm

Ensemble predictor: gradient boosting

Ensemble predictor: gradient boosting

Training Period

1st January 2021 to 15th January 2024

1st January 2021 to 15th January 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

netinterchage

(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).

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All error metrics are in $/MWhr.

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

92.22

92.37

91.70

83.87

85.02

0.15

-0.52

QLD1

214.73

238.09

202.77

225.16

206.22

23.36

-11.95

SA1

36.86

51.16

46.38

46.37

45.49

14.30

9.52

TAS1

37.43

42.49

39.79

19.24

19.92

5.06

2.36

VIC1

17.34

25.25

21.75

25.92

27.50

7.91

4.41

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 Model

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

89.0

70.8

60.6

72.2

74.6

-18.2

-28.4

QLD1

203.4

175.6

424.8

202.6

391.5

-27.8

221.3

SA1

31.1

45.4

79.0

51.8

83.0

14.3

47.9

TAS1

35.4

44.5

32.2

22.6

23.3

9.1

-3.1

VIC1

13.0

30.5

24.8

37.1

38.8

17.4

11.7

Info

Variables that imply binding interconnectors (such as limits and marginal values) are currently not included as features.

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