vpp-ai-platform/contracts/schema/forecast_request.json
Thomas Bayes 8796faca63 M2: skill contracts, Python skill service, L2 eval harness with baseline
- packages/domain: ForecastRequest, BidOptimizationRequest/Result,
  ReportRequest, SkillReport (+ golden and invalid fixtures, exported to
  contracts/ and regenerated as pydantic models).
- skills-py/vpp_skills: FastAPI service with versioned registry; load/PV/
  price forecasts (same-day-type EWM point forecast, conformal residual
  quantiles — coverage test as acceptance gate); bid-optimization MILP on
  HiGHS (binary block participation, hard ledger energy bounds, exact
  Decimal fit of the rounded curve inside the bounds, revenue distribution
  over quantile paths); report generator whose every figure is a
  {tool_call_id, path} reference, with a verifier. 48 tests incl. hypothesis
  property test that bids respect ledger constraints.
- packages/services: LedgerService.dayAheadBounds (the P7 cascade band
  handed to the optimizer); Decimal resolved once for CJS/ESM interop.
- packages/evals: L2 metrics (MAPE, nRMSE, coverage, direction accuracy,
  naive/hindsight revenue baselines), HTTP skill client, rolling-origin
  harness that pushes each bid through the real ledger, CLI with
  --check/--write-baseline; committed baseline on the SYNTHETIC dataset
  (no historical Hubei data yet — baselines measure the harness, not KPI).
- CI: evals job boots the skill service and fails on baseline digest drift.
- docs/open-questions: A6 (flexibility marginal cost = offer floor); A4/B6
  wired as placeholders. README/CLAUDE.md status → M2 done, M3 next.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01UoYoGYzHkFyv3ALenkRPhA
2026-09-02 06:29:08 -04:00

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JSON

{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"type": "object",
"properties": {
"kind": {
"type": "string",
"enum": [
"LOAD",
"PV",
"PRICE"
]
},
"market_date": {
"type": "string",
"pattern": "^\\d{4}-\\d{2}-\\d{2}$"
},
"unit": {
"type": "string",
"enum": [
"mw",
"yuan_per_mwh"
]
},
"history": {
"minItems": 1,
"type": "array",
"items": {
"type": "object",
"properties": {
"interval_minutes": {
"type": "number",
"const": 15
},
"date": {
"type": "string",
"pattern": "^\\d{4}-\\d{2}-\\d{2}$"
},
"values": {
"minItems": 96,
"maxItems": 96,
"type": "array",
"items": {
"type": "string",
"pattern": "^-?\\d+(\\.\\d+)?$"
}
}
},
"required": [
"interval_minutes",
"date",
"values"
],
"additionalProperties": false
}
},
"exogenous": {
"type": "object",
"propertyNames": {
"type": "string"
},
"additionalProperties": {
"type": "object",
"properties": {
"interval_minutes": {
"type": "number",
"const": 15
},
"date": {
"type": "string",
"pattern": "^\\d{4}-\\d{2}-\\d{2}$"
},
"values": {
"minItems": 96,
"maxItems": 96,
"type": "array",
"items": {
"type": "string",
"pattern": "^-?\\d+(\\.\\d+)?$"
}
}
},
"required": [
"interval_minutes",
"date",
"values"
],
"additionalProperties": false
}
},
"features_snapshot_ref": {
"type": "string",
"pattern": "^[0-9a-f]{64}$"
}
},
"required": [
"kind",
"market_date",
"unit",
"history",
"exogenous",
"features_snapshot_ref"
],
"additionalProperties": false,
"$id": "https://vpp-ai-platform/contracts/forecast_request.json",
"title": "ForecastRequest"
}