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

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Formulating a model

A An MLA is used to formulate a model. A model is basically effectively a big equation that consumes real time predispatch and STPASA data to forecast price. Formulating a model takes a long time and retraining a known model (using new historical information) takes many hours. However once a model has been formulated, running it only takes a few seconds.

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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. For example the MLA “random forest regression” is great for forecasting a value for a system that follows a well behaved distribution. However spot prices do not follow a well behaved distribution hence the selected MLA must be flexible enough to predict relatively frequent outlier outcomes.

• 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 then experiment with and determine the set various sets 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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