Intelligent hydropower : Making hydropower more efficient by utilizing machine learning for inflow forecasting

Abstract: Inflow forecasting is important when planning the use of water in a hydropower plant. The process of making forecasts is characterized by using knowledge from previous events and occurrences to make predictions about the future. Traditionally, inflow is predicted using hydrological models. The model developed by the Hydrologiska Byråns Vattenbalansavdelning (HBV model) is one of the most widely used hydrological models around the world. Machine learning is emerging as a potential alternative to the current HBV models but needs to be evaluated. This thesis investigates machine learning for inflow forecasting as a mixed qualitative and quantitative case study. Interviews with experts in various backgrounds within hydropower illustrated the key issues and opportunities for inflow forecasting accuracy and laid the foundation for the machine learning model created. The thesis found that the noise in the realised inflow data was one of the main factors which affected the quality of the machine learning inflow forecasts. Other notable factors were the precipitation data from the three closest weather stations. The interviews suggested that the noise in the realised inflow data could be due to faulty measurements. The interviews also provided examples of additional data such as snow quantity measurements and ground moisture levels which could be included in a machine learning model to improve inflow forecast performance. One proposed application for the machine learning model was as a complementary tool to the current HBV model to assist in making manual adjustments to the forecasts when considered necessary. The machine learning model achieved an average Mean Absolute Error (MAE) of 1.39 compared to 1.73 for a baseline forecast for inflow to the Lake Kymmen river system 1-7 days ahead over the period 2015-2019. For inflow to the Lake Kymmen river system 8-14 days ahead the machine learning model achieved an average MAE of 1.68 compared to 2.45 for a baseline forecast. The current HBV model in place had a lower average MAE than the machine learning model over the available comparison period of January 2018.