Probabilistic Forecasting of Wind Turbine Icing Related Production Losses Using Quantile Regression Forests

Jennie Molinder, Sebastian Scher, Erik Nilsson, Heiner Körnich, Hans Bergström and Anna Sjöblom
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Jennie Molinder: Department of Earth Sciences, Uppsala University, SE-75236 Uppsala, Sweden
Sebastian Scher: Bolin Centre for Climate Research and Department of Meteorology, Stockholm University, SE-106 91 Stockholm, Sweden
Erik Nilsson: Department of Earth Sciences, Uppsala University, SE-75236 Uppsala, Sweden
Heiner Körnich: Unit for Meteorology Research, SMHI, SE-60176 Norrköping, Sweden
Hans Bergström: Department of Earth Sciences, Uppsala University, SE-75236 Uppsala, Sweden
Anna Sjöblom: Department of Earth Sciences, Uppsala University, SE-75236 Uppsala, Sweden

Energies, 2020, vol. 14, issue 1, 1-19

Abstract: A probabilistic machine learning method is applied to icing related production loss forecasts for wind energy in cold climates. The employed method, called quantile regression forests, is based on the random forest regression algorithm. Based on the performed tests on data from four Swedish wind parks available for two winter seasons, it has been shown to produce valuable probabilistic forecasts. Even with the limited amount of training and test data that were used in the study, the estimated forecast uncertainty adds more value to the forecast when compared to a deterministic forecast and a previously published probabilistic forecast method. It is also shown that the output from a physical icing model provides useful information to the machine learning method, as its usage results in an increased forecast skill when compared to only using Numerical Weather Prediction data. A potential additional benefit in machine learning for some stations was also found when using information in the training from other stations that are also affected by icing. This increases the amount of data, which is otherwise a challenge when developing forecasting methods for wind energy in cold climates.

Keywords: wind energy; icing on wind turbines; machine learning; probabilistic forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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