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.
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:
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.