Neural Load Forecast Optimizer
Generate probabilistic load forecasts with confidence intervals and anomaly detection
What You Can Do
You can process complex multivariate utility datasets to identify non-linear relationships between weather, seasonality, calendar effects, and electricity demand. The skill generates calibrated probabilistic forecasts with confidence intervals (10th, 50th, 90th percentiles) that support risk-aware dispatch decisions, unit commitment planning, and reserve procurement—while flagging anomalies and structural shifts in consumption behavior for manual investigation.
Features
generates 10th, 50th, and 90th percentile predictions instead of single-point estimates for better risk quantification
incorporates weather sensitivity, seasonal patterns, calendar effects, and grid events in demand modeling
identifies unusual load patterns and structural breaks requiring operational attention
separates controllable vs. uncontrollable load components for reserve planning
diagnoses forecast accuracy by customer class, time-of-use period, or geographic region
creates probabilistic weather-driven operational scenarios for planning under uncertainty
processes multiple data sources typical in utility operations (SCADA, weather, market, calendar)
Example Output
1-Hour to 14-Day Load Forecast:
- Hour 14: 4,250 MW (10th percentile) | 4,680 MW (50th percentile) | 5,120 MW (90th percentile)
- Confidence range: 870 MW spread accounting for temperature and wind variability
- Anomaly flag: Tuesday peak 18% above seasonal normal—investigate demand response event
Forecast Decomposition:
- Base load (uncontrollable): 3,200 MW
- Weather-sensitive (HVAC): 800 MW (±350 MW range)
- Solar offset: -120 MW (±80 MW cloud variability)
- Recommended reserve margin: 450 MW (upper confidence interval buffer)
Bias Report by Segment:
- Residential: -2.1% MAPE (slight under-forecast in winter)
- Commercial: +1.8% MAPE (slight over-forecast in transition seasons)
What's Included
- SKILL.md: Complete instruction file with forecasting methodology and parameter guidance
- Historical data template: Structured format for load, weather, and grid event inputs
- Forecast decomposition framework: Separates demand components for operational insights
- Anomaly investigation checklist: Step-by-step guide for validating flagged unusual patterns
- Bias analysis workflow: Diagnostic queries to identify systematic forecast errors by customer segment
Who It's For
- Grid operators and system operators — optimizing unit commitment and reserve margin decisions
- Utility forecasters and demand analysts — generating day-ahead and medium-term load predictions
- Market operations teams — submitting accurate demand forecasts for electricity market participation
- Planning engineers — analyzing structural changes in load patterns (EV adoption, solar impacts, demand response)
- Risk and compliance managers — quantifying operational uncertainty and reserve adequacy
Best For
- 1-hour to 14-day ahead load forecasting for dispatch and market planning
- Probabilistic scenario generation for weather-driven operational uncertainty
- Forecast accuracy diagnostics and bias analysis by customer segment or region
- Anomaly detection and investigation following unusual grid events or weather patterns
- Structural break identification in consumption behavior from technology adoption or policy changes
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