The multi-year collaboration is the first of its kind, bringing together Envision Energy, the European Centre for Medium-Range Weather Forecasts (ECMWF), the Met Office (the UK’s national weather service), and Aarhus University (BTECH CET) to collaborate on new methods, models and technologies in order to advance research in renewable energy forecasting.
The partnership has the scientific expertise, supercomputing resources, wind and solar generation data, and partner ecosystems to develop and validate innovative new power forecasting methods and models.
Improving forecasting models can increase the accuracy of energy resource assessments and improve the ability of the grid to plan for renewable energy inputs, helping to lower the levelised cost of wind and solar energy.
“The purpose of this alliance is to work together to advance scientific knowledge in renewable energy forecasting, to accelerate the adoption of sustainable energy solutions globally” said Envision founder and CEO Lei Zhang. “Envision is committed to creating a sustainable future through developing advanced technologies in an open and collaborative way. We are dedicated to making clean, affordable and sustainable energy a reality”.
Erik Andersson, Deputy Director of Forecasts of the European Centre for Medium-Range Weather Forecasts, added that combining the technology, scientific knowledge and data generating of the partners in the collaboration the ability to support renewable energy forecasting can be improved and that in the long run this will help increase the energy industry’s ability to utilise renewable energy sources. This research will demonstrate the value of ensemble forecasting and further enhance the use of ECMWF weather forecasts in the renewable energies sector with a positive impact on utilities, grid operators, and most importantly energy consumers.
The initial phase of the research collaboration has focussed on improving wind and solar power generation forecast accuracy using state-of-the-science data, models, algorithms and supercomputing technologies.
Subsequent research phases are expected to extend this work into enhanced forecasting services for power grids, e-mobility, and optimised energy management for buildings, industrial microgrids and smart cities.
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