Hydropower Decision-Support with Machine Learning and Satellite Driven Forecasts

Hydropower operators rely on forecasts for planning and operational decision-making, but the inaccuracy of current forecasts has implications for revenue, risk, and regulatory requirements. Our proposal leverages machine learning and satellite imagery innovations to improve the accuracy and reliability of hydrologic forecasts. Using a distributed machine learning model, we are able to efficiently incorporate satellite earth observations, model uncertainty, and enable hydropower operators to better plan and manage their assets while reducing risk. In addition to the hydropower utility market, we have had early validation that our approach has application for conservation NGOs, source water utilities, government agencies, and agriculture - illustrating the long-term commercialization potential of our project for a wide range of stakeholders. Our project will address the following central challenges for existing forecasting methods: Existing models underperform compared with machine learning prototypes, existing models do not take advantage of all available satellite earth observations, and existing models do not take advantage of all available dynamic weather data. To address the challenges, in Phase I of our project we will incorporate satellite data into a spatially distributed neural network model, providing a detailed understanding of current land surface conditions and enabling the model to produce more accurate forecasts., account for uncertainty and variability in weather forecasts by integrating ensemble weather forecasts from multiple weather forecasts to create a more accurate probabilistic prediction, perform a large scale forecast verification and benchmarking exercise to demonstrate the value provided by satellite data driven machine learning hydrology forecasts. Ultimately, the resulting technology will be used to inform reservoir operations optimization and enable hydropower operators to maximize revenue and reduce risk, while supporting environmental aims and meeting regulatory requirements.