new r&d



  • Research and innovation action
  • Duration: 42 months
  • Start: November 2019
  • Overall Budget: 4 M€
  • Consortium formed by 12 partners from 6 countries: Portugal, France, Denmark, Greece, Germany and Netherlands.


Smart4RES is a project whose main goal is to develop the next generation of modelling tools for short-term forecasting of weather-dependent RES plants’ power output with improved accuracy and validate their utilization for enabling RES to provide ancillary services (frequency and voltage controls) as well as to optimize the coordination of renewable generation with storage devices.

SmartRES will develop an inclusive approach by bringing expertise from meteorology, physics, applied mathematics, data science and power systems.

The main objectives of the project are:

1. To define requirements for forecasting technologies to enable near 100% RES penetration by 2030 and beyond;
2. To develop a RES-dedicated view of weather forecasting, leading to improvements in forecasting, of the relevant weather variables in the order of 10-15% using various sources of data (satellite, all-sky imagers, NWPs), and the development of very high-resolution forecasting approaches;
3. To develop a new generation of RES forecasting tools that are able to improve RES power production forecasting by at least 15%;
4. To streamline the process of getting optimal value from data and forecasts, through new forecasting products and data marketplaces, and novel business models;
5. To develop new data-driven optimisation and decision-aid tools for enabling the large-scale penetration of renewable energy, combined with storage, into the electricity market as well as to provide system services towards TSOs and DSOs;
6. Validation of new models in living labs and assessment of forecasting values vs remedies.

NEW R&D/EDP scope

NEW R&D will be a main participant of Smart4RES. Our main contributions will be:

  • Contribute to the design forecasting techniques based on a perspective approach (i.e. beyond the state-of-the-art predictive approach) and identify the functional requirements from need-owners;
  • Support the development of forecast models for very short-term forecast horizons so as to target improved renewable energy predictability up to 20%;
  • Validate the added value of the proposed approach with a set of use cases as well as their replicability and scalability with regard to economic, social and environmental performance;
  • Lead the work package “Business Cases Assessing Forecasting Value”.