OpenSTEF

OpenSTEF is an open-source framework for short-term forecasting of electricity demand and generation for power system planning and operations (energy analytics).

• Short-term forecasting of electricity load and generation using data-driven models (energy analytics, forecasting).
• Supports grid operators and energy stakeholders in operational planning and congestion management (grid operations support).
• Open, reusable tooling for building, training, and running forecasting workflows (data science workflow framework).
• Designed to work with real-world operational grid and market data from utilities and system operators (operational data integration).
• Community-governed under LF Energy for shared development across utilities, DSOs, TSOs, and energy organizations (open-source collaboration).

More About OpenSTEF

OpenSTEF is an LF Energy project that provides an open-source framework for short-term forecasting of electricity demand and generation to support grid operation and planning (energy analytics, forecasting). It addresses the need for transparent and reusable forecasting tooling as electricity systems integrate more distributed energy resources, electrified loads, and variable renewable generation. The project focuses on the operational time horizon, where accurate forecasts can support congestion management, dispatch decisions, and capacity planning by grid operators and other energy stakeholders.

The core capability of OpenSTEF is the creation and operation of short-term forecasting models for power system variables such as load and generation (forecasting framework). It is designed as a data-driven environment where users can ingest historical and real-time data, configure models, train them on operational datasets, and deploy them into forecasting workflows. The framework targets use cases where forecasts are required at regular intervals and for specific assets, feeders, regions, or portfolios in a power system context.

From an enterprise perspective, OpenSTEF functions as a specialized analytics layer that can sit alongside existing grid control, market, and data platforms (enterprise energy analytics). It can be integrated with utility data warehouses, time-series databases, or operational data hubs, enabling organizations to feed grid measurements, market information, and weather or contextual data into forecasting pipelines. The outputs can support decision processes in distribution and transmission system operations, flexibility management, or planning teams that require consistent, traceable short-term forecasts.

Technically, OpenSTEF aligns with common data science and Machine Learning (ML) practices (machine learning operations). It structures workflows for data preprocessing, model training, validation, and deployment, and is intended to be used programmatically within modern analytics environments. Organizations can embed OpenSTEF within containerized or cloud-based infrastructures, orchestrate it with existing scheduling and pipeline tools, and connect it via APIs or data interfaces that match their enterprise architecture standards.

Within the LF Energy ecosystem, OpenSTEF occupies the category of short-term energy forecasting and grid analytics (energy analytics, grid operations support). It complements other grid digitalization efforts by providing a common open-source basis for forecasting rather than each utility or operator building isolated, proprietary solutions. For enterprises, this offers a shared, transparent codebase that can be inspected, extended, and adapted to local requirements, while still remaining aligned with an LF Energy-governed project that is oriented toward utility-grade operational use.