Summary

pd4castr is a price forecasting web service that inserts input data into a model to generate a price forecast. A model is basically a very big equation that has been formulated by a machine learning algorithm (MLA).

Initially pd4castr will consume AEMO predispatch data however in time the User will be able to select the input data source, and/or adjust the data (much like price sensitivities), select the model or even generate their own model.

The user interface of pd4castr is designed so that the User can quickly inspect and compare forecasts across variations in input data including rundatetime and source as well as the model used. The input data are similarly presented so that forecasts can be calibrated and contextualised by the User. \

Model performance metrics will be provided so that efficacy can be assessed.

Use Case

We anticipate that pd4castr will produce far more accurate forecasts than AEMO’s predispatch price forecast when the forecast period is still some time away. This is because the pd4castr model factors in bidding behaviour and other dynamics whereas AEMO’s price forecast process follows a strict protocol.

We also anticipate that as the forecast period approaches AEMO’s price forecast will provide a better indicator of likely price outcome - if you think about it, dispatch - forecast period time zero - is kind of like AEMO’s perfect forecast since it is (nearly) always a 100% correct.

User Interface

pd4castr is a price forecasting web service that uses a model that consumes input data to generate a price forecast.

A model is basically a very big equation that has been produced by a machine learning algorithm (MLA). Creating a model involves choosing a range of variables and then selecting historical periods (called training data) that are then inserted into the MLA. The training data include both input variables and the desired output - in this case price. The MLA then creates an equation that will most closely predict the desired output. There is a lot of time and skill involved in creating a new model, a MLA needs to be chosen (there are many algorithms available) that will accommodate the dynamics particular to the system being modelled. Then the MLA needs to be 'tuned' in a variety of ways (called hyper parameterisation). Finally the set of input variables needs to be selected that will capture the primary value drivers to predict the price.