HydroMind provides state-of-the-art accuracy inflow forecasts for asset owners and power traders, which is important for optimizing hydro power production. HydroMind combines weather data (such as precipitation, soil moisture, snow coverage, etc.) from numerical weather prediction (NWP) models, satellites and ground measurements with advanced machine learning methods that learn both local and global weather patterns. Processing of this spatio-temporal information allows HydroMind to produce forecast for individual run-of-the-river hydro plants or reservoirs to regional catchment areas with state-of-the-art accuracy. Compared to traditional forecasting methods, HydroMind achieves a significant reduction in forecast errors, which lowers the imbalance costs from trading operations. The specific reservoir characteristics are modeled implicitly by the machine learning method. Therefore, HydroMind does not rely on static data, such as reservoir form and landscape, but learns the effect of these parameters from observed data. This results in higher accuracy than compared to physical models where uncertainty regarding static variables is translated to the forecast.
HydroMind continuously incorporates the latest weather and inflow information to provide frequent forecast updates. In addition, performance on the first few hours ahead can be further reduced by adding live inflow data, although this is not a requirement. Through probabilistic power forecasts, the forecast uncertainty assessed. This gives higher degree of preparation for power market operations and allows for optimized intraday balancing of run-of-the-river hydro power. HydroMind is provided as a cloud service and thus requires no installation, it can be easily integrated with your current system setup through API or SFTP-server.