Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation

Valentina Sessa, Edi Assoumou, Mireille Bossy and Sofia G. Simões
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Valentina Sessa: MINES ParisTech, Centre de Mathématiques Appliquées (CMA), Sophia Antipolis, 06560 Paris, France
Edi Assoumou: MINES ParisTech, Centre de Mathématiques Appliquées (CMA), Sophia Antipolis, 06560 Paris, France
Mireille Bossy: Université Côte d’Azur, Inria, CNRS, 06560 Sophia Antipolis, France
Sofia G. Simões: LNEG—Laboratório Nacional de Energia e Geologia, I.P. Estrada da Portela, Bairro do Zambujal Ap 7586, 2610-999 Amadora, Portugal

Clean Technol., 2021, vol. 3, issue 4, 1-23

Abstract: Analyzing the impact of climate variables into the operational planning processes is essential for the robust implementation of a sustainable power system. This paper deals with the modeling of the run-of-river hydropower production based on climate variables on the European scale. A better understanding of future run-of-river generation patterns has important implications for power systems with increasing shares of solar and wind power. Run-of-river plants are less intermittent than solar or wind but also less dispatchable than dams with storage capacity. However, translating time series of climate data (precipitation and air temperature) into time series of run-of-river-based hydropower generation is not an easy task as it is necessary to capture the complex relationship between the availability of water and the generation of electricity. This task is also more complex when performed for a large interconnected area. In this work, a model is built for several European countries by using machine learning techniques. In particular, we compare the accuracy of models based on the Random Forest algorithm and show that a more accurate model is obtained when a finer spatial resolution of climate data is introduced. We then discuss the practical applicability of a machine learning model for the medium term forecasts and show that some very context specific but influential events are hard to capture.

Keywords: energy modeling; machine learning; hydropower generation (search for similar items in EconPapers)
JEL-codes: Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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