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

Autor: Yousefi M; Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Science, Bergen, Norway. Mojtaba.yousefi@Hvl.no., Wang J; Department of Electric Power Engineering, Norwegian University of Science and Technology, Trondheim, Norway., Fandrem Høivik Ø; Lyse Produksjon AS, Stavanger, Norway., Rajasekharan J; Department of Electric Power Engineering, Norwegian University of Science and Technology, Trondheim, Norway., Hubert Wierling A; Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Science, Bergen, Norway., Farahmand H; Department of Electric Power Engineering, Norwegian University of Science and Technology, Trondheim, Norway., Arghandeh R; Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Science, Bergen, Norway.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Apr 29; Vol. 13 (1), pp. 7016. Date of Electronic Publication: 2023 Apr 29.
DOI: 10.1038/s41598-023-34133-8
Abstrakt: 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).
(© 2023. The Author(s).)
Databáze: MEDLINE
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