vpp-ai-platform/contracts/schema/forecast_request.json

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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
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"type": "object",
"properties": {
"kind": {
"type": "string",
"enum": [
"LOAD",
"PV",
"PRICE"
]
},
"market_date": {
"type": "string",
"pattern": "^\\d{4}-\\d{2}-\\d{2}$"
},
"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"
}