Templates/Energy & Utilities/Energy demand forecaster
Energy demand forecaster
Per-interval load forecast with confidence band. Reviewer-gate on out-of-envelope predictions routes to control-room analyst.
What this template is
Pattern: forecast classifier producing a closed-enum load-band per zone per interval + confidence. Conservative band-boundary mapping; reviewer-gate fires when predictions exceed historical envelope (drift detection). Spec hash + modelIdentity per L12 entry feed AI Act Annex III §2 critical-infrastructure technical documentation + REMIT trading-AI reproducibility for ACER inquiries.
The template
TypeScript · BSL-1.1 · License
import { RuntimeAISpec } from '@promethean/runtime-ai';
export const demandForecasterSpec: RuntimeAISpec = {
specId: 'demand-forecaster-v1',
displayName: 'Energy demand forecaster',
description:
'Per-interval load-band forecast for grid-zone + market window.',
category: 'classifier',
schemaVersion: 'promethean-runtime-ai-spec-1.0',
canonicalForm: 'v1',
inputSchema: {
fields: [
{ name: 'forecastId', type: 'string', required: true,
redaction: 'hash-only' },
{ name: 'gridZone', type: 'string', required: true,
maxLength: 32 },
{ name: 'targetIntervalIso', type: 'string', required: true,
maxLength: 32 },
{ name: 'weatherForecastHash', type: 'string', required: true,
redaction: 'hash-only' },
{ name: 'historicalLoadMean', type: 'number', required: true,
min: 0, max: 100_000 },
],
},
outputSchema: {
fields: [
{ name: 'loadBandMW', type: 'enum', required: true,
enumValues: [
'band-0-100', 'band-100-500', 'band-500-1000',
'band-1000-5000', 'band-5000-plus',
] },
{ name: 'pointEstimateMW', type: 'number', required: true,
min: 0, max: 100_000 },
{ name: 'confidence', type: 'number', required: true,
min: 0, max: 1 },
{ name: 'driftFlagged', type: 'boolean', required: true },
],
},
promptTemplate: {
system:
'You forecast electricity demand for grid-zone + market-window. ' +
'Output a load-band + point estimate (MW) + confidence. ' +
'Set driftFlagged=true if the estimate is >2σ from historical mean.',
user:
'Forecast {{forecastId}} for zone {{gridZone}}, interval {{targetIntervalIso}}. ' +
'Historical load mean: {{historicalLoadMean}} MW.',
},
modelIdentity: {
provider: 'anthropic',
model: 'claude-sonnet-4-5',
version: '20250929',
},
reviewerGate: 'on-low-confidence',
lowConfidenceThreshold: 0.75,
maxLatencyMs: 2000,
fallbackBehavior: 'queue-for-review',
};
Regulations addressed
This template's configuration choices map to specific regulatory obligations. The substrate doesn't certify compliance — but the spec hash + reviewer-verdict + modelIdentity per L12 entry give you the evidence layer for these citations:
- ·EU AI Act Annex III §2 — critical infrastructure
- ·NIS2 Annex I — energy as essential sector
- ·REMIT Regulation 1227/2011 — wholesale energy markets
- ·Network code 2017/1485 — system operation
Installation + usage
- Create a free Dev-tier workspace — API key + Ed25519 signing key issued instantly.
- Install the SDK:
npm install https://promethean.software/runtime-ai/latest.tgz. - Paste the template above into your codebase. Adjust
modelIdentity+ prompt for your context. - Call
runConstrainedAI(spec, input, { client, receiptLogPath, productId, signingKey })from your service code. For local testing passcreateMockRuntimeAIClient(spec); for production, an Anthropic / OpenAI / Azure adapter. - Verify the chain with the Apache-2.0 verifier.