OWGRE – Optimized weather-related green energy production and consumption

As the energy system is becoming more complex and weather dependent, weather forecasts are of essential importance for transforming the energy landscape towards decarbonization. In this project, we combine probabilistic numerical weather prediction with machine learning algorithms in order to provide optimized decision support for green energy production and consumption.

Probabilistic forecasts have been underused in this context due to complexity related to their interpretation and data volume. To solve this we will create a machine-readable data portal coupled to machine learning algorithms. We will tailor improved weather forecasts to better support the needs of green energy applications.

Our smart solutions optimize specific energy systems based on the latest weather forecast, local observations, and real-time production data from renewable energy. Targeted applications are heating, ventilation and air-conditioning in buildings, battery charging, and solar and wind power applications.

Main objectives

We utilize current knowledge in numerical modelling, data analysis and digitalization to provide optimized decision support for green energy production and consumption by:

  • Improving probabilistic short-range weather forecasts.
  • Increase energy efficiency with data-driven machine-learning.
  • Creating standardized data format and tools for data exchange.
  • Exploiting the energy value chain in a transnational market.

Project organisation 

The project is coordinated by Swedish Meteorological and Hydrological Institute (Sweden).

Project manager: Tomas Landelius

Project partners 

Algorithm Energy AB (Sweden)

Rebasian Technologies AB (Sweden)

Finnish Meteorological Institute  (Finland)

Nuuka Solutions Oy (Finland)

Estonian Environmental Agency (Estonia)

Project duration

December 2022 – June 2025. 

Project funding

This project has been awarded funding within the ERA-Net SES Joint Call 2020 for transnational research, development and demonstration projects.