Rebase Energy is a Python-based energy forecasting platform that enables the creation, deployment, and monitoring of customizable energy models for distributed energy systems. The platform addresses the need for accurate demand and renewable energy forecasts, helping energy market professionals optimize operations in a volatile trading environment.
Funding
$120K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Energy market professionals face increasing competitiveness due to shorter trading horizons and volatility from weather-driven renewable production, creating a need for accurate demand and renewable energy forecasts. Existing energy forecasting solutions often lack the flexibility and scalability required to handle the complexities of distributed energy systems.
Solution
Rebase Energy offers a Python-based platform designed to streamline the creation, deployment, and monitoring of customizable energy forecasting models. The platform provides tools to develop accurate energy forecasting models, enabling energy market professionals to optimize operations in a volatile trading environment. Users can leverage open-source algorithms and a Python SDK to build fully customizable models tailored to their specific needs. The platform also facilitates backtesting and forecastability evaluations, and provides access to multiple weather forecast providers through a single API.
Target Audience
The platform is designed for energy market professionals, including energy traders, distribution system operators (DSOs), district heating operators, power producers, aggregators, and energy service providers.
Features
- Python-first SDK for building custom energy forecasting models
- Support for various machine learning frameworks, including LightGBM
- API access to multiple weather forecast providers
- Tools for backtesting and forecastability evaluations
- Scalable infrastructure for deploying and monitoring models at scale
- Integrations for demand, solar, and wind forecasts