Latest 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)
Note that more variables are used as inputs into the model than are presented in the Model Input Chart in the Home page of pd4castr. Chart variables are representative of the key variables of the model but changes in other variables may also impact the model forecast.
Variables that imply binding interconnectors (such as limits and marginal values) are currently not included as features.
Target:
dispatchprice for each region
Training period
From March 2018 to 27th of 30th March 2023.
List of features:
Predispatchregionsum fields used (including all regions) |
|---|
hours_out |
rrp |
dispatchableload |
netinterchange |
rolling_rrp |
availablegeneration |
dispatchablegeneration |
ss_solar_clearedmw |
ss_solar_uigf |
ss_wind_clearedmw |
ss_wind_uigf |
totaldemand |
totalintermittentgeneration |