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Publikācija: ANN-Based Forecasting of Hydropower Reservoir Inflow

Publication Type Full-text conference paper published in conference proceedings indexed in SCOPUS or WOS database
Funding for basic activity Unknown
Defending: ,
Publication language English (en)
Title in original language ANN-Based Forecasting of Hydropower Reservoir Inflow
Field of research 2. Engineering and technology
Sub-field of research 2.2 Electrical engineering, Electronic engineering, Information and communication engineering
Authors Antans Sauļus Sauhats
Romāns Petričenko
Zane Broka
Kārlis Baltputnis
Dmitrijs Soboļevskis
Keywords ANN; forecasting; hydropower; reservoir inflow
Abstract Reservoir inflow forecasting with artificial neural networks is presented in this paper. Different types of ANN input data were considered such as temperature, precipitation and historical water inflow. Performance of the hourly inflow forecasts was assessed based on a case study of a specific hydropower reservoir in Latvia. The results showed that all the approaches had similar prediction errors implying that for optimal hydropower scheduling uncertainties need to be modelled which is also proposed in this study through generation of several forecast realisations in addition to point predictions.
DOI: 10.1109/RTUCON.2016.7763129
Hyperlink: http://ieeexplore.ieee.org/document/7763129/ 
Reference Sauhats, A., Petričenko, R., Broka, Z., Baltputnis, K., Soboļevskis, D. ANN-Based Forecasting of Hydropower Reservoir Inflow. In: 2016 57th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON 2016): Proceedings, Latvia, Riga, 13-14 October, 2016. Piscataway, NJ: IEEE, 2016, pp.267-272. ISBN 978-1-5090-3732-2. e-ISBN 978-1-5090-3731-5. Available from: doi:10.1109/RTUCON.2016.7763129
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