TY - JOUR

T1 - The impact of imperfect weather forecasts on wind power forecasting performance: Evidence from two wind farms in Greece

AU - Spiliotis, Evangelos

AU - Petropoulos, Fotios

AU - Nikolopoulos, Kostas

PY - 2020/4/12

Y1 - 2020/4/12

N2 - 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

AB - 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

KW - forecasting

KW - uncertainty

KW - wind power

KW - machine learning

KW - weather forecasts

U2 - 10.3390/en13081880

DO - 10.3390/en13081880

M3 - Article

VL - 13

JO - Energies

JF - Energies

SN - 1996-1073

IS - 8

M1 - 1880

ER -