Day-ahead wind power forecasting based on feature extraction integrating vertical layer wind characteristics in complex terrain

Abstract

Accurate wind power forecasts help establish efficient power supply plans and stabilize power systems. For long-term forecasts, the outputs of numerical weather prediction (NWP) models are pipelined as inputs for the statistical post-processing model, underscoring the necessity of understanding forecasts simulated from NWP to enhance power prediction accuracy.This study aims to enhance the quality of wind power forecasts in complex terrains, focusing on identifying and processing appropriate wind features of vertical layers simulated by NWP. In complex terrains with significant terrain variability, it is crucial to meticulously analyze and select the optimal vertical layer for each site or turbine individually, as simulated wind speeds at higher vertical layers than those used in previous research could potentially yield stronger correlations. Furthermore, we introduce a data processing technique that integrates wind characteristics across vertical layers, utilizing Principal Component Analysis (PCA). This approach not only provides physically intuitive results but also demonstrates enhanced performance compared to other feature selection methods. By selecting the appropriate vertical layers and applying the proper feature extraction, for wind farms situated in complex terrains in Korea, the annual normalized mean absolute error can be reduced by up to 1.2 %.