The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece

Evangelos Spiliotis, Fotios Petropoulos and Konstantinos Nikolopoulos ()
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Evangelos Spiliotis: Forecasting and Strategy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
Fotios Petropoulos: School of Management, University of Bath, Bath BA2 7AY, UK

Energies, 2020, vol. 13, issue 8, 1-18

Abstract: Weather variables are an important driver of power generation from renewable energy sources. However, accurately predicting such variables is a challenging task, which has a significant impact on the accuracy of the power generation forecasts. In this study, we explore the impact of imperfect weather forecasts on two classes of forecasting methods (statistical and machine learning) for the case of wind power generation. We perform a stress test analysis to measure the robustness of different methods on the imperfect weather input, focusing on both the point forecasts and the 95% prediction intervals. The results indicate that different methods should be considered according to the uncertainty characterizing the weather forecasts.

Keywords: forecasting; uncertainty; wind power; machine learning; weather forecasts (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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