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

Authors: Valentina Sessa; Edi Assoumou; Mireille Bossy; Sofia G. Simões;

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

Abstract

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. info:eu-repo/semantics/publishedVersion

Countries
France, Portugal, France
Keywords

energy modeling; machine learning; hydropower generation, Hydropower generation, hydropower generation, General Engineering, Environmental engineering, TA170-171, Environmental technology. Sanitary engineering, machine learning, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], energy modeling, Machine learning, Energy modeling, [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA], TD1-1066

23. De Felice, M.; Dubus, L.; Suckling, E.; Troccoli, A. The impact of the North Atlantic Oscillation on European hydropower generation. arXiv 2018. [CrossRef]

24. Guyon, I.; Elisseeff, A. An introduction to variable and feature selection. J. Mach. Learn. Res. 2003, 3, 1157-1182.

25. Janitza, S.; Hornung, R. On the overestimation of random forest's out-of-bag error. PLoS ONE 2018, 13, e0201904. [CrossRef] [PubMed] [OpenAIRE]

26. Ashraf, F.B.; Haghighi, A.T.; Riml, J.; Alfredsen, K.; Koskela, J.J.; Kløve, B.; Marttila, H. Changes in short term river flow regulation and hydropeaking in Nordic rivers. Sci. Rep. 2018, 8, 17232. [CrossRef] [PubMed]

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    4
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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visibility
download
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
4
Top 10%
Average
Average
71
46
Green
gold
Funded by
EC| ERA4CS
Project
ERA4CS
European Research Area for Climate Services
  • Funder: European Commission (EC)
  • Project Code: 690462
  • Funding stream: H2020 | ERA-NET-Cofund
Related to Research communities
Sustainable Development Solutions Network - Greece
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