Correlated Time-Series in Multi-Day-Ahead Streamflow Forecasting Using Convolutional Networks

• Published in: IEEE access Vol. 8; pp. 215748 - 215757
• Main Authors: Barino, Felipe O., Silva, Vinicius N. H., Lopez-Barbero, Andres P., De Mello Honorio, Leonardo, Santos, Alexandre Bessa Dos
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Atmospheric models; Autoregressive processes; convolutional neural networks; Correlation; Data models; Discharges (electric); Electric power generation; Forecasting; Hydroelectric plants; Hydroelectric power; Hydrogeology; Hydrology; hydropower scheduling; Madeira River; Mathematical models; Power plants; Predictive models; Production planning; River flow; Rivers; Stream flow; Time series analysis; Time series forecasting
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3040942

Information about future streamflow is important for hydropower production planning, especially for damless hydro-power plants. The river flow is a reflection of various hydrological, hydrogeological, and meteorological factors, which increases the direct modeling difficulty, and favors the use of data-driven methods. In this paper, we propose the use of one-dimensional convolutional neural networks (1d-CNN) for multi-day ahead river flow forecasting and we present a multi-input model using correlated-input time-series. The proposed model was applied at the Madeira River, the Amazon's largest and most important tributary, near the Santo Antônio damless hydro-power plant. We compared the proposed correlated-input 1d-CNN to a single-input 1d-CNN model and some baseline models. Furthermore, we conclude that 1d-CNN performed better than all baseline models and that the correlated-input forecasting model is 5 times smaller than the single-input equivalent with accuracy improvements.