Generation of hydro inflow datasets

· ENTSO-E ·

The aim of the project was to generate a consistent hydro inflow dataset that could be used by ENTSO-E in all power systems studies, for more than 30 countries.

HelioSwitch developed algorithms for regression and analysis of hydroelectric power plant production and river discharge data. The tool developed involves the following 3 main steps:

processing of hydroelectric generation data which includes, on top of standard quality check and anomaly detection, an advanced frequency analysis to detect spurious signal in the data provided;
extraction of location-specific river discharge data coupled with a latent space analysis, highlighting the physical phenomenon underlying the raw data;
generation of nonlinear transfer functions for each hydroelectric power plant between river discharge data, and the water inflow at the power plants, inferred from their production.

Hydroelectric run-of-river generation forecasting

· Terna ·

HelioSwitch developed high-scalability probabilistic machine learning models for Terna, aimed at forecasting the generation of Italy's run-of-river hydroelectric power plants.

Our team successfully developed a scalable algorithm that predicts power production from run-of-river hydropower plants, utilizing minimal registry information (location, size, plant type), weather data, and historical generation data. The algorithm provided hourly forecasts for a one-week prediction horizon and evaluated the possibility of extending the horizon to two weeks.

Delta-PRA reconstruction algorithms

· Illumia ·

HelioSwitch developed and tested algorithms to reconstruct the hourly values of PODs leaving PRA aggregation from their monthly integrals.

We developed and tested machine learning methodologies with Illumia to infer the hourly dynamics of PODs in the delta-PRA, starting from their monthly integral, in order to improve PRA forecast accuracy.