load-forecast v1.0.0A

Immutable. This exact content is served forever at /api/v1/blob/e5de53dd81eefde9.

---
name: load-forecast
description: Analyze energy load forecasting systems including demand prediction models, weather integration, peak shaving, demand response, and renewable intermittency handling.
version: "1.0.0"
category: analysis
platforms:
  - CLAUDE_CODE
---

You are in AUTONOMOUS MODE. Do NOT ask questions. Evaluate every component of the energy load forecasting system systematically.

TARGET:
$ARGUMENTS

If no arguments provided, analyze the entire load forecasting codebase in the current working directory.

============================================================
PHASE 0: SYSTEM DISCOVERY
============================================================

Auto-detect the load forecasting system architecture:

TECH STACK:
- `requirements.txt` / `pyproject.toml` -> Python (statsmodels, prophet, tensorflow, pytorch, scikit-learn, darts)
- `pom.xml` / `build.gradle` -> Java/Scala (Spark, Flink, Hadoop-based pipelines)
- `package.json` -> Node.js (API layer, dashboard, scheduling)
- `go.mod` -> Go (high-throughput data ingestion, microservices)
- `docker-compose.yml` / `k8s/` -> Container orchestration, service mesh
- `dbt_project.yml` / `profiles.yml` -> dbt data transformation layer
- `airflow.cfg` / `dags/` -> Airflow orchestration

FORECASTING COMPONENTS:
- Identify prediction models: ARIMA/SARIMA, Prophet, LSTM/GRU, gradient boosting, transformer models
- Identify data ingestion: SCADA feeds, smart meter AMI data, weather APIs, ISO/RTO market feeds
- Identify feature stores: time-series databases (InfluxDB, TimescaleDB, QuestDB, Druid)
- Identify orchestration: Airflow, Prefect, Dagster, cron-based scheduling
- Identify serving layer: REST API, gRPC, message queue (Kafka, RabbitMQ)
- Identify visualization: Grafana, custom dashboards, reporting pipelines
- Identify grid integration: OASIS feeds, ICCP protocol, DNP3/Modbus interfaces

Produce a system architecture map before proceeding.

============================================================
PHASE 1: TIME-SERIES DATA PIPELINE
============================================================

Evaluate the data ingestion and preparation layer:

DATA SOURCES:
- Identify all data sources: AMI smart meters, SCADA telemetry, weather stations, ISO market data
- Check data polling intervals (1-min, 5-min, 15-min, hourly) against forecasting granularity
- Verify data source redundancy and failover handling
- Check for data source authentication and secure transport (TLS, VPN tunnels)
- Verify historical data retention policies (minimum 3 years for seasonal models)

DATA QUALITY:
- Check for missing data detection and imputation strategies
- Verify outlier detection: voltage spikes, meter malfunctions, negative readings
- Check for data validation rules: physical bounds, rate-of-change limits
- Verify timestamp alignment across heterogeneous data sources
- Check for daylight saving time handling in time-series alignment
- Verify data completeness metrics and alerting on gaps

FEATURE ENGINEERING:
- Check for calendar features: hour-of-day, day-of-week, month, holidays, special events
- Verify lagged features: load at t-1, t-24, t-168 (same hour last week)
- Check for rolling statistics: moving averages, rolling std, min/max over windows
- Verify weather-derived features: heating degree days (HDD), cooling degree days (CDD)
- Check for economic indicator integration: industrial production, GDP proxies
- Verify feature scaling and normalization consistency between training and inference
- Check for feature importance analysis and periodic feature selection review

DATA STORAGE:
- Verify time-series database selection is appropriate for data volume and query patterns
- Check for data partitioning strategy (by time range, by zone, by customer class)
- Verify retention policies with automatic downsampling for older data
- Check for backup and disaster recovery of historical forecasting data
- Verify query performance for training data extraction at scale

For each finding: file path, component, severity, description, recommendation.

============================================================
PHASE 2: FORECASTING MODEL EVALUATION
============================================================

Evaluate the prediction models:

STATISTICAL MODELS:
- Identify ARIMA/SARIMA implementations and parameter selection methodology
- Check for seasonal decomposition: STL, X-13ARIMA-SEATS, or custom decomposition
- Verify stationarity testing (ADF test, KPSS test) before model fitting
- Check for exponential smoothing (Holt-Winters) as baseline or complement
- Verify model order selection: AIC/BIC criteria, cross-validation, or manual tuning
- Check for regime switching models handling structural breaks (COVID, plant outages)

MACHINE LEARNING MODELS:
- Identify gradient boosting implementations (XGBoost, LightGBM, CatBoost)
- Check feature set completeness for ML models vs statistical models
- Verify hyperparameter tuning methodology (grid search, Bayesian optimization, Optuna)
- Check for ensemble approaches combining multiple model families
- Verify cross-validation strategy respects temporal ordering (no future data leakage)
- Check for model interpretability (SHAP values, feature importance plots)

DEEP LEARNING MODELS:
- Identify LSTM/GRU/Transformer implementations for sequence modeling
- Check input sequence length and prediction horizon configuration
- Verify attention mechanism usage for long-range temporal dependencies
- Check for multi-task learning (simultaneous forecasts at multiple horizons)
- Verify GPU utilization and training pipeline efficiency
- Check for transfer learning from pre-trained temporal models
- Verify early stopping and regularization to prevent overfitting

MODEL HIERARCHY:
- Check for hierarchical forecasting: system → zone → substation → feeder → customer
- Verify forecast reconciliation (top-down, bottom-up, or optimal reconciliation)
- Check for probabilistic forecasting: prediction intervals, quantile regression
- Verify ensemble aggregation methodology (simple average, weighted, stacking)
- Check that individual model contributions are tracked and monitored

FORECAST HORIZONS:
- Identify supported forecast horizons: real-time (minutes), day-ahead, week-ahead, seasonal, long-term
- Verify appropriate model selection per horizon (statistical for short, ML/DL for medium/long)
- Check for model switching logic between horizons
- Verify forecast update frequency matches operational requirements

============================================================
PHASE 3: WEATHER INTEGRATION
============================================================

Evaluate weather data integration for load prediction:

WEATHER DATA SOURCES:
- Identify weather data providers: NOAA, ECMWF, Weather Company, OpenWeatherMap, Dark Sky
- Check for multiple weather model ingestion (GFS, NAM, HRRR, ECMWF IFS)
- Verify weather station mapping to service territory zones
- Check for weather forecast ensemble handling (multiple model runs)
- Verify weather data refresh frequency matches forecast update cycle

TEMPERATURE MODELING:
- Check for temperature-load relationship modeling (piecewise linear, polynomial)
- Verify heating/cooling breakpoint estimation (typically 65F/18C, but should be calibrated)
- Check for humidity correction: heat index, wet-bulb temperature, apparent temperature
- Verify wind chill factor integration for winter heating load
- Check for solar radiation impact on cooling load (cloud cover, UV index)
- Verify temperature forecast blending from multiple weather models

EXTREME WEATHER HANDLING:
- Check for extreme temperature scenarios in forecasting (heat waves, polar vortex)
- Verify storm impact modeling: ice storms, hurricanes, severe thunderstorms
- Check for demand response integration during extreme weather events
- Verify load shedding scenario modeling
- Check for weather-driven renewable generation correlation with load

WEATHER FORECAST UNCERTAINTY:
- Verify weather forecast uncertainty propagation to load forecast confidence intervals
- Check for scenario generation using weather ensemble members
- Verify degradation of weather forecast accuracy at longer horizons is accounted for
- Check for weather analog day selection methodology

============================================================
PHASE 4: PEAK SHAVING AND DEMAND RESPONSE
============================================================

Evaluate peak demand management capabilities:

PEAK DETECTION:
- Check for coincident peak prediction (system peak, zonal peak, customer peak)
- Verify peak day-ahead alerting and notification systems
- Check for transmission cost allocation (ICAP tag, network service peak load)
- Verify historical peak analysis and trend detection
- Check for peak probability scoring and confidence thresholds

DEMAND RESPONSE INTEGRATION:
- Check for DR program modeling: direct load control, interruptible tariffs, curtailment
- Verify DR event dispatch optimization (which resources, when, how much)
- Check for customer baseline load (CBL) calculation methodology
- Verify measurement and verification (M&V) of DR event performance
- Check for DR resource availability forecasting
- Verify integration with DR management systems (OpenADR, proprietary platforms)

BATTERY STORAGE OPTIMIZATION:
- Check for battery energy storage system (BESS) dispatch optimization
- Verify state-of-charge modeling and degradation tracking
- Check for arbitrage optimization: charge during off-peak, discharge during peak
- Verify co-optimization with renewable generation forecasts
- Check for battery cycling constraints and warranty limit enforcement

ECONOMIC DISPATCH:
- Check for marginal cost calculation at different load levels
- Verify generation unit commitment integration
- Check for transmission constraint modeling
- Verify locational marginal pricing (LMP) correlation with load forecasts
- Check for import/export capacity constraint handling

============================================================
PHASE 5: RENEWABLE INTERMITTENCY HANDLING
============================================================

Evaluate integration of variable renewable energy sources:

SOLAR GENERATION FORECASTING:
- Check for solar irradiance forecasting (GHI, DNI, DHI components)
- Verify cloud cover impact modeling on PV generation
- Check for solar panel degradation and soiling factors
- Verify inverter efficiency curves and clipping modeling
- Check for distributed vs utility-scale solar forecast aggregation

WIND GENERATION FORECASTING:
- Check for wind speed and direction forecasting at hub height
- Verify power curve modeling for wind turbines (manufacturer curves vs empirical)
- Check for wake effect modeling in wind farm configurations
- Verify ramp event detection and prediction (rapid wind changes)
- Check for icing and curtailment impact on wind generation

NET LOAD FORECASTING:
- Verify net load calculation: gross load minus behind-the-meter solar, minus wind
- Check for duck curve handling (steep evening ramp from solar decline)
- Verify ramping requirement forecasting for dispatchable generation
- Check for minimum generation constraint handling during high renewable periods
- Verify forecast error correlation between load and renewable generation

RENEWABLE UNCERTAINTY:
- Check for probabilistic renewable generation forecasts
- Verify reserve requirement calculation based on renewable forecast uncertainty
- Check for scenario-based planning with renewable penetration growth
- Verify curtailment forecasting when generation exceeds load plus export capacity

============================================================
PHASE 6: MODEL VALIDATION AND OPERATIONS
============================================================

Evaluate model performance monitoring and operational readiness:

ACCURACY METRICS:
- Check for standard metrics: MAPE, MAE, RMSE, normalized RMSE
- Verify metrics are computed at appropriate aggregation levels (system, zone, customer class)
- Check for peak-specific accuracy metrics (accuracy during top 10 load hours)
- Verify probabilistic forecast evaluation: CRPS, pinball loss, reliability diagrams
- Check for forecast bias detection and correction mechanisms

BACKTESTING:
- Verify walk-forward backtesting implementation (no future data contamination)
- Check for seasonal backtesting coverage (summer peak, winter peak, shoulder seasons)
- Verify backtesting against extreme weather events in historical data
- Check for comparison against naive baselines (persistence, same-day-last-week)

MODEL RETRAINING:
- Check for automated retraining pipeline and schedule
- Verify retraining triggers: performance degradation, concept drift, data distribution shift
- Check for champion-challenger model deployment strategy
- Verify rollback capability if new model underperforms
- Check for model versioning and experiment tracking (MLflow, Weights & Biases, Neptune)

OPERATIONAL MONITORING:
- Check for real-time forecast vs actual comparison dashboards
- Verify alerting on forecast errors exceeding thresholds
- Check for data pipeline health monitoring and alerting
- Verify forecast delivery SLAs and monitoring
- Check for operator override capability with audit trail

============================================================
OUTPUT
============================================================

## Load Forecasting System Analysis Report

**System:** [name/description]
**Stack:** [detected technologies]
**Forecast Horizons:** [detected horizons]
**Service Territory:** [if detectable from configuration]

### Summary

| Category | Status | Findings | Critical |
|----------|--------|----------|----------|
| Data Pipeline | [PASS/WARN/FAIL] | N | N |
| Forecasting Models | [PASS/WARN/FAIL] | N | N |
| Weather Integration | [PASS/WARN/FAIL] | N | N |
| Peak/Demand Response | [PASS/WARN/FAIL] | N | N |
| Renewable Intermittency | [PASS/WARN/FAIL] | N | N |
| Validation/Operations | [PASS/WARN/FAIL] | N | N |

### Model Performance Matrix

| Model | Horizon | MAPE | RMSE | Peak Accuracy | Status |
|-------|---------|------|------|---------------|--------|
| [model name] | [horizon] | [%] | [MW] | [%] | [GOOD/FAIR/POOR] |

### Feature Coverage Matrix

| Feature Category | Present | Quality | Gap |
|-----------------|---------|---------|-----|
| Calendar/temporal | | | |
| Weather (temperature) | | | |
| Weather (solar/wind) | | | |
| Economic indicators | | | |
| Lagged load values | | | |
| Rolling statistics | | | |
| Special events | | | |

### Detailed Findings

For each category with WARN or FAIL:

#### [Category Name]

| # | Severity | File | Description | Impact | Recommendation |
|---|----------|------|-------------|--------|----------------|

### Forecast Accuracy Assessment
- **Day-ahead accuracy:** [findings]
- **Week-ahead accuracy:** [findings]
- **Peak prediction accuracy:** [findings]
- **Renewable forecast accuracy:** [findings]

### Remediation Priority
[Ordered list by operational impact and reliability risk]

============================================================
NEXT STEPS
============================================================

After reviewing the analysis:
- "Run `/grid-optimizer` to analyze distribution network optimization alongside load forecasts."
- "Run `/commodity-pricing` to evaluate how load forecasts feed into energy trading systems."
- "Run `/energy-compliance` to review regulatory reporting derived from forecast data."
- "Run `/arch-review` to evaluate system architecture for scalability and reliability."
- "Run `/load-test` to stress test the forecast serving API under peak query load."

============================================================
DO NOT
============================================================

- Do NOT modify any forecasting models, weights, or hyperparameters — this is an analysis skill.
- Do NOT execute model training or retraining pipelines.
- Do NOT access or display actual customer meter data or billing information.
- Do NOT expose grid topology details, substation locations, or critical infrastructure identifiers in output.
- Do NOT skip renewable intermittency analysis even for systems without owned renewable assets.
- Do NOT assume model accuracy without checking validation methodology for data leakage.
- Do NOT conflate training metrics with production performance — verify holdout/live evaluation exists.