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.

Target:

dispatchprice by region

Training period

From March 2018 to 27th of November 2022.

List of features:

From Predispatch tables

HOURS_OUT,
PD_RRP_NSW1, PD_RRP_QLD1, PD_RRP_SA1, PD_RRP_TAS1, PD_RRP_VIC1,
DISPATCHABLELOAD_NSW1, DISPATCHABLELOAD_QLD1, DISPATCHABLELOAD_SA1, DISPATCHABLELOAD_TAS1, DISPATCHABLELOAD_VIC1, NETINTERCHANGE_NSW1, NETINTERCHANGE_QLD1, NETINTERCHANGE_SA1, NETINTERCHANGE_TAS1, NETINTERCHANGE_VIC1,
RRP_ROLLING_NSW1, RRP_ROLLING_QLD1, RRP_ROLLING_SA1, RRP_ROLLING_TAS1, RRP_ROLLING_VIC1,
SCHEDULED_AVAIL_NSW1, SCHEDULED_AVAIL_QLD1, SCHEDULED_AVAIL_SA1, SCHEDULED_AVAIL_TAS1, SCHEDULED_AVAIL_VIC1,
SCHEDULED_GEN_NSW1, SCHEDULED_GEN_QLD1, SCHEDULED_GEN_SA1, SCHEDULED_GEN_TAS1, SCHEDULED_GEN_VIC1,
SS_SOLAR_CLEAREDMW_NSW1, SS_SOLAR_CLEAREDMW_QLD1, SS_SOLAR_CLEAREDMW_SA1, SS_SOLAR_CLEAREDMW_TAS1, SS_SOLAR_CLEAREDMW_VIC1,
SS_SOLAR_UIGF_NSW1, SS_SOLAR_UIGF_QLD1, SS_SOLAR_UIGF_SA1, SS_SOLAR_UIGF_TAS1, SS_SOLAR_UIGF_VIC1,
SS_WIND_CLEAREDMW_NSW1, SS_WIND_CLEAREDMW_QLD1, SS_WIND_CLEAREDMW_SA1, SS_WIND_CLEAREDMW_TAS1, SS_WIND_CLEAREDMW_VIC1,
SS_WIND_UIGF_NSW1, SS_WIND_UIGF_QLD1, SS_WIND_UIGF_SA1, SS_WIND_UIGF_TAS1, SS_WIND_UIGF_VIC1,
TOTALDEMAND_NSW1, TOTALDEMAND_QLD1, TOTALDEMAND_SA1, TOTALDEMAND_TAS1, TOTALDEMAND_VIC1,
TOTALINTERMITTENTGENERATION_NSW1, TOTALINTERMITTENTGENERATION_QLD1, TOTALINTERMITTENTGENERATION_SA1, TOTALINTERMITTENTGENERATION_TAS1, TOTALINTERMITTENTGENERATION_VIC1, IS_5MS

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