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Aimed at the wind energy sector, RESILIENCE is a user-friendly tool to produce information of the future variability in wind power resources based on probabilistic climate predictions.
RESILIENCE provides seasonal predictions of future wind speed at a global scale together with their forecast quality assessment.

The primary aim of the RESILIENCE prototype is to strengthen the efficiency and security of wind power supply within energy networks, by providing robust information of the future variability in wind power resources based on probabilistic climate predictions.

How do climate predictions advance current practices?

Current practices to estimate future wind resource variability use the retrospective wind speed climatology, with an assumption that the past will also represent the future. Recent advances in climate predictions can provide a more informative view by modelling future wind over near-term timescales (months to years): they use both an analysis of the past climate system, as well as its current state at the specific time when the prediction is created to provide a probability of different future outcomes, with an indication as to which will be the most likely. It has been demonstrated that climate predictions can improve upon using climatology at some spatial and temporal scales, so users now have a new set of climate risk management tools that can strengthen their decision-making, but are they ready to use them?

Climate predictions in decision-making:

Climate predictions come with a new set of challenges for end users: information is often un-tailored and hard to understand or apply in a decision-making context. RESILIENCE will address these challenges using visualisation tools and interfaces tailored to the energy sector. This innovative approach will support the development of a new realm of wind resource assessment services and tools, using state-of-the-art climate predictions, in order to facilitate important decisions.

Tailored information for the energy sector:

Climate components that have a large affect on wind power will be the focus: for example, seasonal variability in wind speeds, wind direction, temperature and air pressure will be explored, as well as climate phenomenon’s known to influence wind such as the North Atlantic Oscillation (NAO) or El Niño Southern Oscillation (ENSO). Geographical regions where the seasonal wind variability can be skilfully predicted will be evaluated and effectively communicated to end users via an innovative visualisation, alongside a demonstration of the potential applications using real predictions over past timescales.

The outcome will illustrate how seasonal wind predictions can lead to a safer, more efficient and therefore cost-effective operation and planning of wind power within the wider energy system.


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