Short-term inflow forecasting in a dam-regulated river in Southwest Norway using causal variational mode decomposition

6Citations
Citations of this article
20Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Climate change affects patterns and uncertainties associated with river water regimes, which significantly impact hydropower generation and reservoir storage operation. Hence, reliable and accurate short-term inflow forecasting is vital to face climate effects better and improve hydropower scheduling performance. This paper proposes a Causal Variational Mode Decomposition (CVD) preprocessing framework for the inflow forecasting problem. CVD is a preprocessing feature selection framework that is built upon multiresolution analysis and causal inference. CVD can reduce computation time while increasing forecasting accuracy by down-selecting the most relevant features to the target value (inflow in a specific location). Moreover, the proposed CVD framework is a complementary step to any machine learning-based forecasting method as it is tested with four different forecasting algorithms in this paper. CVD is validated using actual data from a river system downstream of a hydropower reservoir in the southwest of Norway. The experimental results show that CVD-LSTM reduces forecasting error metric by almost 70% compared with a baseline (scenario 1) and reduces by 25% compared to an LSTM for the same composition of input data (scenario 4).

Cite

CITATION STYLE

APA

Yousefi, M., Wang, J., Fandrem Høivik, Ø., Rajasekharan, J., Hubert Wierling, A., Farahmand, H., & Arghandeh, R. (2023). Short-term inflow forecasting in a dam-regulated river in Southwest Norway using causal variational mode decomposition. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-34133-8

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free