Abstract
This paper contributes to forecasting of renewable infeed for use in dispatch scheduling and power systems analysis. Ensemble predictions are commonly used to assess the uncertainty of a future weather event, but they often are biased and have too small variance. Reliable forecasts for future inflow are important for hydropower operation, and the main purpose of this work is to develop methods to generate better calibrated and sharper probabilistic forecasts for inflow. We propose to extend Bayesian model averaging with a varying coefficient regression model to better respect changing weather patterns. We report on results from a case study from a catchment upstream of a Norwegian power plant during the period from 24 June 2014 to 22 June 2015.
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Acknowledgements
The authors would like to thank Stein-Erik Fleten for valuable discussions. We also thank Statkraft and Stian Solvang Johansen for providing data and motivation behind this work. Andreas Kleiven acknowledges support from the Research Council of Norway, through HydroCen, project number 257588. For Ingelin Steinsland this project was supported by the Research Council of Norway, project 250362.
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Kleiven, A., Steinsland, I. (2019). Inflow Forecasting for Hydropower Operations: Bayesian Model Averaging for Postprocessing Hydrological Ensembles. In: Helseth, A. (eds) Proceedings of the 6th International Workshop on Hydro Scheduling in Competitive Electricity Markets. HSCM 2018. Springer, Cham. https://doi.org/10.1007/978-3-030-03311-8_5
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DOI: https://doi.org/10.1007/978-3-030-03311-8_5
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