Use Cases

Rassket is built for five energy-specific problems. Each use case gets specialized feature engineering, a purpose-built prediction approach, and results explained in the context of your grid and your utility.

1. Day-Ahead Load Forecasting

What is this? Predicting system load or demand for the next trading day — and similar short-to-medium horizons like 48-hour or weekly demand.

What Rassket does:

  • Automatically creates time-of-use window features — peak, shoulder, and off-peak indicators
  • Adds public holiday indicators for your region
  • Generates lag features — load from 24 hours ago, 7 days ago, and seasonal equivalents
  • Applies weather interaction terms where temperature and humidity data is present
  • Trains with time-aware cross-validation so the model never sees future data during training

What you get:

  • Hourly or sub-hourly load forecast with upper and lower confidence intervals
  • Feature importance — which hours, days, and seasonal signals drive the forecast
  • PDF report and CSV export ready for your operations team

2. Solar / Wind Generation Forecasting

What is this? Predicting variable renewable output — including curtailment risk and generation uncertainty — for dispatch planning and grid balancing.

What Rassket does:

  • Builds weather interaction features from temperature, cloud cover, and wind speed where available
  • Generates seasonality features capturing daily and annual solar cycles
  • Creates curtailment-aware lag features for wind generation patterns
  • Produces forecast intervals that capture the wider uncertainty typical of renewable generation

What you get:

  • Generation forecast with uncertainty bands
  • Feature importance explaining which weather signals drive generation
  • Export to CSV and PDF for integration with dispatch planning tools

3. Energy Theft / Meter Fraud Detection

What is this? Detecting anomalies, meter tampering, diversion, and non-technical losses in metering data — without manually reviewing every account.

What Rassket does:

  • Identifies consumption patterns that deviate significantly from expected behavior
  • Builds features from meter reading sequences that flag sudden drops or inconsistencies
  • Applies domain-appropriate detection methods trained on your actual metering data
  • Ranks accounts by anomaly score so your team can prioritize investigations

What you get:

  • Anomaly scores with confidence levels for each account or meter
  • Feature importance showing which signal patterns drove the detection
  • Export-ready list for field investigation teams

4. Predictive Maintenance (Turbines)

What is this? Detecting early warning signs of asset failure in rotating equipment — turbines, generators, and other field assets — before the failure happens.

What Rassket does:

  • Processes vibration, temperature, pressure, and other condition monitoring signals
  • Creates rolling statistics — moving averages and standard deviations — that surface degradation trends
  • Builds lag features that capture how asset health is changing over time
  • Detects deviations from normal operating ranges before they become failures

What you get:

  • Condition score over time with confidence intervals
  • Feature importance showing which signals are most predictive of failure
  • PDF maintenance report and API output for CMMS integration

5. Short-Term Electricity Price Forecasting

What is this? Predicting LMP, hub prices, or tariff-linked prices over short horizons — for procurement, trading, and rate optimization.

What Rassket does:

  • Models price movements using load, generation mix, and historical price signals
  • Creates time-of-use and congestion-pattern features relevant to your market
  • Applies your utility's operational context to focus on the price signals that matter for your grid
  • Produces confidence intervals that capture price spike risk

What you get:

  • Price forecast with upper and lower bounds
  • Feature importance explaining which market signals drive the forecast
  • CSV and PDF export for procurement and trading teams
Rassket energy forecasting results dashboard showing forecast curves and feature importance for a load forecasting use case

Rassket delivers forecast curves, confidence intervals, and plain-English explanations for each energy use case.

Not sure which use case fits your data? Choose the one that most closely matches your primary goal. You can always clear and restart with a different use case. The domain selection screen explains each option in plain English.

What All Use Cases Share

  • No coding required — connect your data and select your use case; Rassket handles everything else
  • Utility context applied automatically — your operational context shapes every forecast and explanation
  • Plain-English results — forecasts and feature importance explained without technical jargon
  • Export-ready outputs — PDF, CSV, and API for every use case
  • Under 30 minutes to first forecast — from raw data to production-ready results

Getting Started

  1. Go to Getting Started to upload your data or connect a database
  2. Select your energy use case and utility on the Domain Selection screen
  3. Follow the How It Works guide through preprocessing, training, and results