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pd4castr forecasts are derived in a matter of seconds by a model which is formulated by Machine Learning Algorithm (MLA) trained with historical data. This model effectively anticipates changes in participant behaviour based on known market conditions and prior to actual changes in participant behaviour in real time.

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Current model details

Machine learning algorithm details

Algo type:

Random Forest Regression

Hyper parameters:

No weighting across time periods. It is likely that we’ll add weighting to more recent training periods.

To be disclosed with later release stages.

Input variables (features)

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• The MLA: First you must understand the plethora of available MLAs and choose one that best matches the problem you’re trying to solve.

• The Features: Features is the name given to the input variables used to predict the target (price forecast). Once you’ve selected your MLA you need to experiment with and determine the set of features you will use to train the MLA.

• Training periods: Once you’ve selected the MLA and Features then you need to determine the historical time periods to train the MLA. You can also weight time periods, for example you may weight recent time periods more than distant periods.

• Tuning: There are a number of Hyper Parameters that you need to experiment with and tune in order to derive a meaningful model. These hyper parameters are part of the MLA.

• Performance: Finally you need to assess the performance of each model by testing the model and comparing it with other models and finally with AEMO predispatch.

• Release: Once you’re satisfied with a model you can release it to production. In time we expect there to be more than one model that the user may select which will produce a price forecast in real time.

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Retraining the model

Models need to be routinely retrained. Typically the retraining will simply include the most recent historical market information. I.e. market outcomes that has come to pass since the last retraining effort. However retraining could involve any of the steps listed in “formulating a model” (above).

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