Day-Ahead Grid Load Forecast Validation Checklist
Prepare a comprehensive verification checklist to validate day-ahead electricity load forecasts before wholesale market submission.
Use this template when operational analytics teams need to cross-examine short-term load projections against telemetry, weather shocks, and historical baselines. It helps grid operators and forecasting leads prevent costly market imbalance penalties.
Role: Senior Power Systems Forecasting Analyst with 15 years of experience in transmission balancing and short-term load modeling.
Context
- Target Utility: {{utility_name}}
- Operational Zone: {{grid_balancing_zone}}
- Prediction Window: {{forecast_horizon_hours}} hours
- Meteorological Feed: {{weather_model_source}}
- Baseline History: {{historical_baseline_days}} days
- Planned Outages & Maintenance: {{outage_schedule_summary}}
Task
Generate a structured, actionable pre-submission checklist for validating day-ahead electricity demand forecasts to ensure grid reliability and minimize settlement variance.
Method
- Review input telemetry integrity for all SCADA meters across {{grid_balancing_zone}} to identify missing or stuck data points.
- Correlate temperature and humidity forecasts from {{weather_model_source}} with historical non-linear cooling/heating degree days.
- Verify model adjustments against {{outage_schedule_summary}} to confirm planned load drops are accurately subtracted.
- Benchmark the candidate forecast against similar day profiles from the {{historical_baseline_days}} lookback period.
- Evaluate peak hour timing and ramp-rate gradients across the {{forecast_horizon_hours}} window against operational limits.
- Flag anomalies where confidence intervals breach historical variance thresholds.
- Document approval gates required by {{utility_name}} before passing forecasts to market bidding systems.
Constraints
- MUST format every checklist item as a binary checkable task with a clear pass/fail criterion.
- MUST include explicit tolerance limits (percentage or MW) for every variance check.
- MUST NOT recommend changes to model hyperparameters or source code.
- Content MUST focus strictly on operational verification for the defined {{forecast_horizon_hours}} horizon.
- Items MUST be grouped logically by analytical phase.
Output format
Return a markdown checklist organized into three distinct sections: 1. Input Data & Weather Verification, 2. Model Output & Variance Checks, and 3. Sign-off & Dispatch Submission. Each section must contain exactly 3 to 5 markdown task boxes (- [ ]) followed by a one-sentence failure mitigation action.
Self-review
- Confirm all 6 variables are referenced naturally within the checklist context.
- Verify every check item has an actionable, objective criteria rather than subjective guidance.
- Ensure total item count does not exceed 15 checklist items.
Explicit role, a named task, and discrete steps the model can follow.
Background, inputs and variables the model needs before it starts.
Hard boundaries — what the model must and must not do.
A named, field-level shape for the response.
Ordered work items that force analysis before an answer.
Length and structure that travel across frontier models.
Signal density — instruction weight without padding.
Documented variables so the scaffold adapts to new inputs.
Quality bar, assumptions and behaviour when inputs are thin.
How much real usage the template has behind it.