Introduction

What is Rassket?

Rassket delivers energy forecasts, plain-English explanations, and export-ready results in one place.

Rassket is automatic prediction software built specifically for energy teams. It connects to your data, learns from it, and produces accurate forecasts — without requiring any coding, data science expertise, or dedicated ML staff.

Unlike generic tools, Rassket understands the energy sector. It knows that day-ahead load forecasting behaves differently from solar generation forecasting. It knows your utility's operational context. It applies the right approach automatically, so you get results that are relevant to your grid and your customers.

Who is Rassket For?

• Energy analysts: Build accurate forecast models without writing code
• Grid operations teams: Get load, generation, and price forecasts fast
• Utility executives: Make data-driven decisions with plain-English summaries
• Asset managers: Detect equipment issues and anomalies before they become failures
• Planning teams: Model demand scenarios and infrastructure needs with confidence

What Problems Does Rassket Solve?

1. Time to first forecast

Traditional forecasting workflows take weeks of data preparation, feature engineering, and model tuning. Rassket compresses all of that into under 30 minutes — from raw data to a production-ready forecast.

2. Technical barriers

Building good energy prediction models normally requires Python expertise, knowledge of gradient boosting frameworks, and experience with time-series cross-validation. Rassket handles all of that automatically. You choose your use case; Rassket handles the rest.

3. Generic tools that don't understand energy

General-purpose forecasting tools don't know about time-of-use windows, public holidays, weather interactions, or seasonal demand patterns. Rassket applies these automatically based on the energy domain you select.

4. Results you can't explain or share

Rassket doesn't just produce numbers. Every forecast comes with confidence intervals, feature importance explained in plain English, and export-ready reports your team can present to stakeholders.

Supported Energy Use Cases

Rassket supports five energy-specific domains:

• Day-ahead load forecasting — System load or demand for the next trading day and similar horizons
• Solar / wind generation forecast — Variable renewable output, curtailment, and generation uncertainty
• Energy theft / meter fraud detection — Anomalies, diversion, and non-technical loss patterns in metering data
• Predictive maintenance (turbines) — Asset health, failures, and condition signals for rotating equipment
• Short-term electricity price forecast — LMP, hub, or tariff-linked price movements over short horizons

Performance

Security & Compliance

• Encrypted at rest and in transit — All data is protected with industry-standard encryption
• SOC 2 Type II certification in progress — Independent security audit underway
• GDPR aligned — Data handling practices are aligned with GDPR requirements
• On-premise deployment available — Enterprise customers can run Rassket within their own infrastructure
• Full audit trail — Every training run is logged with a complete, reproducible audit trail

How It Works — Overview

1. Upload or Connect — Upload a file or connect a live database (SCADA, PostgreSQL, InfluxDB, MySQL, MongoDB, Prometheus)
2. Select Energy Domain — Choose your use case and utility context
3. Preprocessing — Rassket validates, cleans, and prepares your data automatically
4. Model Building — Automated training with time-aware cross-validation and feature engineering
5. Insights & Action — Forecast curves, plain-English explanations, and export-ready results

Continue to Getting Started to upload your data and run your first forecast.