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pd4castr price forecasts aim to be more accurate than AEMO forecasts, particularly when the forecast period is many hours into the future. AEMO price forecasts change considerably from the first predispatch to later predispatch runs as the forecast period approaches. This is primarily caused by participant's unit commitments and the repricing of volume that best matches their position.
pd4castr forecasts are quickly derived in a matter of seconds by a model which is formulated by training a 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,
the process of applying MLAs to formulate a model,
the re-training process including input variables, MLA tuning and training periods, and performance metrics and retraining process.
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Current model details
Input variables (features)
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The diagramme below shows the re-training process.
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Glossary
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