Configuration File

The .pd4castrrc.json file is the central configuration for your model project. It lives at the root of your model directory and defines everything the platform needs to know about your model: its identity, inputs, outputs, sensitivities, and scheduling behavior. The CLI validates this file against a schema on every command.

File location and format

The configuration file must be named .pd4castrrc.json and placed at the root of your model project. It uses standard JSON format. You can generate a starter config by running pd4castr init, which scaffolds a new project from a template.

You can point the CLI to a different config file using the -c or --config flag on any command:

pd4castr test -c .pd4castrrc.staging.json

Complete field reference

Top-level fields

Inputs

The inputs array defines the data your model consumes. Each entry has the following fields:

For inputs with automatic data fetching, add a fetcher block:

See Model inputs for detailed explanations.

Outputs

The outputs array defines the schema of your model’s forecast data.

See Model outputs for detailed explanations.

Sensitivities

The sensitivities array defines alternative scenario runs. Each entry has the following fields:

See Sensitivities and scenarios for detailed explanations.

Input aggregations

The inputAggregations array defines summary views of input data displayed below the forecast chart.

See Sensitivities and scenarios for detailed explanations.

System-managed fields

These fields are prefixed with $$ and are managed automatically by the CLI. Do not modify them manually.

These fields are written back to your .pd4castrrc.json during pd4castr publish. Commit the updated file to version control so your project stays in sync with the platform.

SQL file paths

All query paths in the configuration (fetcher queries, sensitivity queries, input aggregation queries, and runDatetimeQuery) are relative to the project root. The CLI reads these files and inlines the SQL content when you publish.

Full example

Here’s a complete .pd4castrrc.json for a day-ahead price forecast model with fetched inputs, output series, and input aggregations:

{
  "name": "Day Ahead Price Forecast",
  "forecastVariable": "price",
  "timeHorizon": "day_ahead",
  "displayTimezone": "Australia/Brisbane",
  "public": false,
  "runMode": "AUTOMATIC",
  "runDatetimeQuery": "queries/run-datetime.sql",
  "metadata": {
    "resolution": "30min",
    "description": "30-minute day-ahead electricity price forecast"
  },
  "inputs": [
    {
      "key": "dispatch_price",
      "trigger": "WAIT_FOR_LATEST_FILE",
      "uploadFileFormat": "json",
      "targetFileFormat": "json",
      "fetcher": {
        "type": "AEMO_MMS",
        "checkInterval": 300,
        "config": {
          "checkQuery": "queries/data-fetchers/dispatch-price-check.sql",
          "fetchQuery": "queries/data-fetchers/dispatch-price-fetch.sql"
        }
      }
    },
    {
      "key": "regional_boundaries",
      "trigger": "USE_MOST_RECENT_FILE",
      "uploadFileFormat": "csv",
      "targetFileFormat": "csv"
    }
  ],
  "outputs": [
    {
      "name": "forecast_datetime",
      "type": "date",
      "seriesKey": false
    },
    {
      "name": "NSW1",
      "type": "float",
      "seriesKey": true,
      "colour": "#84EDDC"
    },
    {
      "name": "QLD1",
      "type": "float",
      "seriesKey": true,
      "colour": "#FD4E4E"
    },
    {
      "name": "SA1",
      "type": "float",
      "seriesKey": true,
      "colour": "#FED600"
    },
    {
      "name": "TAS1",
      "type": "float",
      "seriesKey": true,
      "colour": "#40A967"
    },
    {
      "name": "VIC1",
      "type": "float",
      "seriesKey": true,
      "colour": "#1965C6"
    }
  ],
  "sensitivities": [
    {
      "name": "High Demand (+10%)",
      "query": "queries/sensitivities/high-demand.sql"
    }
  ],
  "inputAggregations": [
    {
      "name": "Native Demand",
      "query": "queries/input-aggregations/native-demand.sql",
      "description": "Regional demand by demand_and_nonshedgen",
      "colours": ["#008000", "#009900", "#00B300", "#00CC00", "#00E600"]
    }
  ]
}

Next steps

• Model inputs — Detailed guide to input configuration and data fetchers.
• Model outputs — How to define your output schema.
• Sensitivities and scenarios — Scenario analysis and input aggregations.
• Testing your model — Local validation workflow.
• Publishing — How to ship your model.
• Run modes and scheduling — Automatic and on-demand triggering.