English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The accurate, efficient, and reliable forecasting of wind speed is a hot research topic in wind power generation and integration. However, available forecasting models focus on forecasting the wind speed using historical wind speed data and ignore multidimensional meteorological variables. The objective is to develop a hybrid model with multidimensional meteorological variables for forecasting the wind speed accurately. The complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied to handle the nonlinearity of the wind speed. Then, the original wind speed will be decomposed into a series of intrinsic model functions with specified numbers of frequencies. A quadratic model that considers the two-way interactions between factors is used to pursue accurate forecasting. To reduce the model complexity, Gram–Schmidt-based feature selection (GSFS) is applied to extract the important meteorological factors. Finally, all the forecasting values of IMFs will be summed by assigning weights that are carefully determined by the whale optimization algorithm (WOA). The proposed forecasting approach has been applied on six datasets that were collected in Qinghai province and is compared with several state-of-the-art wind speed forecasting models. The forecasting results demonstrate that the proposed model can represent the nonlinearity of the wind speed and deliver better results than the competitors.

Multidimensional Meteorological Variables for Wind Speed Forecasting in Qinghai Region of China: A Novel Approach