Optimizing Hydroelectric Dam Operations with Machine Learning
Hydroelectric power generation, which converts the potential energy of water to electric energy by discharging stored water through generators, is an important renewable energy resource. To avoid overflows, it is necessary to release water in advance when flood is predicted due to heavy rains. Better weather prediction would improve prediction capabilities of river inflows and subsequent dam operations, so that we can reduce unnecessary water release for more efficient hydroelectric power generation.
This study aims to achieve more efficient dam operations using state-of-the-art machine learning techniques. For that purpose, we develop successive three machines as follows. The first machine improves precipitation forecasts by numerical weather prediction, extending the traditional model output statistics (MOS) methods. The second machine emulates a river runoff model and predicts the river inflow using the precipitation forecasts from the first machine. In the first and second machines, we apply supervised learning using observed radar and river inflow data as the references, respectively. Using the reinforcement learning technique, the third machine aims to maximize the total power generation following operational restrictions for safety. The predicted river inflow by the second machine is used in the third machine as input data. We develop these three machines for a certain river catchment. This poster presents our ongoing work on developing machine-learning-based dam operations.
Mr. Marimo Ohhigashi RIKEN