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Hydropower plants

How to maximise the efficiency of hydropower trading and production?

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How to maximise the efficiency of hydropower trading and production?

By avoiding simplified approach purely based on historical averages or on black-box statistical model, that can result wrong estimates and difficulty in interpretation.

A scientific and robust approach, based on multiple data sources affecting the whole catchment, can improve production estimates and avoid penalties in the energy market.

Short-term forecasts for trading

Waterjade improves the efficiency of short-term (+ 5 days) predictions by means of high-resolution weather forecasts. By combining them with artificial intelligence and physical models, we are able to gain a deeper understanding of the water cycle. As a result, the accuracy of the system inputs is enhanced.

Many issues can be addressed thanks to Waterjade:

  • Unbalances in the production plan

  • Fines to be paid to energy authority for failure to comply with the production plan

  • Water waste in case of overflows

  • Flood risk downstream 

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Long-term predictions

Waterjade improves the efficiency of long-term predictions (+6 months) by means of artificial intelligence algorithms and snow monitoring.

Despite being calibrated on historical data, they take as input seasonal forecasts from the most prestigious weather centers. Such data allow Waterjade to halve the error with respect to historical mean. As a consequence, forecasting accuracy is enhanced and water use gains efficiency.

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Estimation of hydropower potential stored in snow

Generally, consistent budgets are dedicated to measurement campaigns aiming at estimating snow water equivalent (SWE). About 8 campaigns are normally held between winter and spring. This kind of approach is particularly demanding in terms of money and responsibility, especially for human resources involved in survey campaigns that may be subject to avalanche risk.

Waterjade improves the efficiency of snow water equivalent (SWE) estimation through remote snow monitoring from satellite data and physical models for snow simulation.

In-situ measurements become thus unnecessary. As a result, costs are cut by up to 60% and operators’ safety is not jeopardised.

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