ANN-Based Forecasting of Hydropower Reservoir Inflow

2016 57th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON 2016): Proceedings 2016
Antans Sauļus Sauhats, Romāns Petričenko, Zane Broka, Kārlis Baltputnis, Dmitrijs Soboļevskis

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.

Keywords
ANN; forecasting; hydropower; reservoir inflow
DOI
10.1109/RTUCON.2016.7763129
Hyperlink
http://ieeexplore.ieee.org/document/7763129/

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

Publication language
English (en)