Project proves that satellite data and AI can accelerate the renewable energy transition

by Jan 26, 2023Case Studies, Company News, Europe, Featured, Projects, Switzerland, Thought Leadership

Swiss hydropower industry stakeholders reap the rewards of highly accurate seasonal forecasts produced through a combination of Wegaw’s near real-time geospatial data fused with Artificial Intelligence and Hydrique Engineer’s long-term hydrological models.

The scientific community already understands the premise and benefit of using satellite data for snowpack information, and thanks to this collaboration, commercial hydropower stakeholders are now realizing the potential too. With the support of the Swiss Federal Office of Energy (SFOE), the 18 month DeFROST4Hydropower project brought nine innovative energy companies together and we are very proud to share that the results successfully demonstrated significantly improved seasonal forecasts, yielding commercial and societal benefits.

Participants of the DeFROST4Hydropower Project in Switzerland

The results of this project boost energy trading capabilities and asset management efficiencies with Snow-Water-Equivalent (SWE) being confirmed as a determining factor for energy companies’ long-term seasonal inflow forecasts. The reason why SWE in particular is an important variable is because it tells us how much water an amount of snow contains. Thus, by having access to highly accurate SWE data in advance, water resource managers may more effectively plan for future water use. For instance, hydroelectric plant operators would have better insights as to when to release or store water, and energy traders would be able to mitigate financial burdens by avoiding volatile day markets.

When integrated into rainfall-runoff models for long-term forecasting, the errors were greatly reduced by up to 50% and the number of trading adjustments were reduced by up to 20%, with some project participants even calculating increased income of up to 1.2% per year on average.


“This DeFROST4Hydropower project has become the bedrock of a climate resilience focused collaboration in the Swiss French hydropower ecosystem. I am very proud of these results and firmly believe that what we’re doing today is key to easing the energy crisis whilst enabling global transformation to a more sustainable renewable energy future for all.

Together with the rainfall-runoff model specialists of Hydrique Ingénieurs, our team produced seasonal forecasts for a dozen different catchment areas (the total area behind the dam, draining water into a reservoir) of large reservoirs and run-of-river power plants situated throughout the Swiss French alpine region, allowing for a wide range of hydrological situations to be evaluated. Whilst the SWE datasets had a really high correlation in comparison to data from a high-resolution sensor, the Snow Height (HS) data specifically was validated based on Ultracam photogrammetry from the Institute for Snow and Avalanche Research (SLF) drone flights.

Optimizing long-term trading with real-time satellite derived snow data
Case in point: By integrating satellite snow data for forecasting and seasonal prediction of water supply in alpine dams, project participant SIG (Services Industriels de Genève) wanted to better manage renewable electricity supply. 

The geospatial data consisted of snow height (HS), Snow Water Equivalent (SWE) and snow cover extent (SCE) was fused with AI before being integrated into rainfall-runoff models. This process proved to be very effective in accurately estimating water supply in spring and summer, enabling improved input predictability and tangible ROI for long-term trading from 60, 90, 120, 150 to 180 days periods.

trading optimization through long-term water snow forecast
The above is an example that demonstrates how the assimilation of Wegaw’s fusion of satellite derived data and AI into inflow models can provide optimal forecasts for long-term lead times of 60 to 180 days periods.

“We discovered that the SWE values are a determining factor for the quality of long-term seasonal inflow forecasts and can also improve forecasting performance by 5%-10%, especially for large catchments and time frames between 60 to 180 days.”


With two thirds of the country being alpine, 1500 lakes and high levels of annual rainfall, Switzerland offers an ideal setting for hydropower. It is therefore no surprise that this renewable energy source accounts for the majority of Switzerland’s domestic electricity production and is a key factor for Swiss energy security. The country’s Energy Strategy 2050 aims to increase the share of hydroelectricity generated, whilst adhering to Swiss water protection legislation and adapting to changing hydrological conditions and other climate change effects.

As well as offering a step towards digitalization and increased sustainability of hydropower, our work on this project underpins the International Energy Agency’s analysis report ‘Renewables 2022’ which states that hydropower is set to remain the primary source throughout the forecast period of 2010-2027 and that renewables are expected to become the primary energy source for electricity generation globally in the next three years.

To find out more about this project, check out our dedicated webinar in which we delved into the highlights and key take-aways with members of the international hydropower ecosystem and our guest speakers from Hydrique, and Alpiq:

webinar on-demand image - optimizing long-term trading 1600px

By having a long-term forecast that is as unbiased as possible and resilient to climate anomalies, we believe that companies can avoid volatile market situations, reduce downside risk and become stronger in today’s more frequent climate anomalies. For example, energy traders can use these optimal long-term forecasting capabilities to sell energy in long-term contracts several months in advance, ensuring income as early as January/February.

You may also get in touch with our team today at and connect with our CEO & Co-Founder, Ion Padilla.

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