Load Forecasting Analysis & Validation
Validate load forecasts and identify demand patterns for grid stability
What You Can Do
You can systematically review historical load data to uncover demand patterns, weather correlations, and anomalies that impact forecast accuracy. Claude helps you validate short-term and day-ahead forecast models, calculate accuracy metrics (MAPE, bias), identify root causes of prediction errors, and recommend model adjustments—enabling you to optimize generation scheduling, maintain adequate reserves, and prevent transmission congestion.
Features
Detects seasonal, weekly, and daily load patterns from historical consumption data with trend analysis
Identifies unusual load spikes, dips, and deviations that signal data quality issues or external events
Maps relationships between temperature, humidity, solar irradiance, and load consumption to improve variable selection
Calculates MAPE, bias, and other accuracy metrics to assess model performance across time horizons
Investigates significant forecast misses (>5% MAPE) to identify contributing factors and operational blind spots
Generates realistic peak demand scenarios for transmission system studies and capacity planning
Evaluates multiple forecast outputs side-by-side to support model selection and operational confidence
Troubleshoots unusual weekday/weekend patterns and investigates emerging demand shifts in your service territory
Example Output
Example 1: Weather Correlation Report
- Temperature: r = 0.82 (strong inverse relationship in summer cooling season)
- Humidity: r = 0.31 (weak correlation, secondary driver)
- Solar Irradiance: r = -0.45 (negative correlation during peak solar generation hours)
- Recommendation: Increase temperature coefficient in day-ahead model for summer months
Example 2: Forecast Accuracy Assessment
- Current Model MAPE: 4.2% (acceptable for day-ahead horizon)
- Peak Hours (16:00-20:00): MAPE 6.8% — Overestimating afternoon ramp
- Off-Peak Hours (23:00-06:00): MAPE 2.1% — Strong baseline accuracy
- Identified Issue: Model lag on load response to temperature drop at sunset
Example 3: Anomaly Summary
- July 15: 12% load spike at 14:00 (unscheduled cooling demand, correlation with heat wave event)
- March 3: 8% weekend dip (holiday effect not captured in baseline model)
- Recommendation: Add holiday calendar and extreme weather threshold flags to preprocessing pipeline
What's Included
- SKILL.md: Complete instruction file with skill overview, use cases, and analysis workflows
- Historical Load Analysis Template: Structured format for organizing consumption data, identifying trends, and documenting pattern findings
- Weather Correlation Framework: Checklist and methodology for mapping weather variables to load drivers
- Forecast Validation Checklist: Step-by-step validation procedure covering data quality, accuracy metrics, error distribution, and model assumptions
- Anomaly Investigation Worksheet: Guided prompts for root-cause analysis of forecast misses and unusual load patterns
Who It's For
- Grid Operators — Validate day-ahead and short-term forecasts to optimize generation scheduling and reserve margins
- Demand Forecasting Analysts — Review model performance, investigate accuracy gaps, and recommend parameter adjustments
- System Planning Engineers — Develop peak load scenarios and validate demand assumptions for transmission studies
- Renewable Energy Integration Specialists — Analyze load patterns to improve net-load forecasting with high solar/wind penetration
- Utility Operations Managers — Support operational decision-making by validating forecast reliability before implementation
Best For
- Validating short-term (1-7 day) and day-ahead forecast models before grid deployment
- Investigating forecast errors exceeding acceptable thresholds (>5% MAPE) in specific hours or days
- Analyzing weather-load correlations to improve model variable selection and coefficient tuning
- Detecting and troubleshooting anomalies in historical load profiles and emerging demand patterns
- Comparing multiple forecast model outputs to support model selection and operational confidence decisions
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