Day-ahead inflow forecasting using causal empirical decomposition
Mojtaba Yousefi, Xiaomei Cheng, Michele Gazzea, August Wierling, Jayaprakash Rajasekharan, Arild Helseth, Hossein Farahmand, Reza Arghandeh
Abstract
It is essential to have accurate and reliable daily-inflow forecasting to improve short-term hydropower scheduling. This paper proposes a Causal multivariate Empirical mode Decomposition (CED) framework as a complementary pre-processing step for a day-ahead inflow forecasting problem. The idea behind CED is combining physics-based causal inference with signal processing-based decomposition to get the most relevant features among multiple time-series to the inflow values. The CED framework is validated for two areas in Norway with different meteorological and hydrological conditions. The validation results show that using CED as a pre-processing step significantly enhances (up to 70%) the forecasting accuracy for various state-of-the-art forecasting methods.