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

Swift development in wind power and extension of wind generation necessitates significant research in numerous fields. Due to this, wind power is weather dependent; it is fluctuating and is sporadic over numerous time periods. Hence, timely wind power prediction is perceived as an extensive contribution to well-grounded wind power prediction with complex patterns. In addition, a number of wind power prediction methods have been developed. For proper planning and operation of power systems with complicated patterns, wind power prediction in an accurate and timely manner is essential. This paper presents a wind power prediction method with feature selection and prediction called, Pearson Autocovariance Distinct Patterns and Attention-based Deep Learning (PACDP-ADL). In the deep learning environment, feature selection plays a crucial aspect and a prediction task. A Pearson Autocovariance Feature Selection model is used for identifying necessary features for wind power prediction and reduces the complexity of the model. Next, an Attention-based Long Short-Term Memory Wind Prediction algorithm is employed to retain required patterns and forget irrelevant patterns to acquire more satisfactory prediction precision. The proposed PACDP-ADL method is validated by utilizing the wind power data with various performance metrics such as wind power accuracy, wind power time, and true positive rate compared with the state-of-the-art method.

Pearson Autocovariance Distinct Patterns and Attention-Based Deep Learning for Wind Power Prediction