Summary
pd4castr is a price forecasting web service that
runs a model formulated by a machine learning algorithm (MLA) to produce a price forecast. A model is essentially a very big equation that a MLA derives by ‘training’ on historical data. pdView selects the input variables, the training periods and the type of MLA (as well as the hyper parameters used in the MLA) so that price forecasts can accurate predict as many features of the energy market as possible.
Input data can include forecast demand, available generation, interconnector flows and so forth. Input data may vary depending on the model.
The user interface of pd4castr is designed to provide a clear view and understanding of the price forecasts including model performance, input data and training periods.
Models are routinely retrained and released when there is a clear improvement in performance.
Forecast Time Periods
Currently only the predispatch time horizon is forecast.
Soon to be released is the week ahead forecast.
User Interface
Model re-training process
pdView periodically creates new models by re-training which can include any of the following changes:
Introducing recent historical market outcomes.
Weighting historical time periods.
Changing or adding input variables.
Changing the machine learning algorithm or the hyper parameters that tune the algorithm.
Use Case
Early and accurate forecasts
pd4castr generates 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 and does not anticipate changes in unit commitment and bidding.
As real time approaches the forecast period, AEMO’s price forecast will likey become a better indicator of price outcome - this is because the dispatch period - which you can think of as forecast period time zero - is by definition (nearly) perfect since it is (nearly) always a 100% correct.
Independent forecast for middle and back office
Ideally risk metrics should be calculated independently of the front office. Where possible why not consume data provided by a reputable third party?
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.