A Deep Attention Convolutional Recurrent Network Assisted by K-shape Clustering and Enhanced Memory for Short Term Wind Speed Predictions

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

View graph of relations

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)856-867
Journal / PublicationIEEE Transactions on Sustainable Energy
Volume13
Issue number2
Online published14 Dec 2021
Publication statusPublished - Apr 2022

Abstract

Due to the increasing penetration of wind energy in nowadays power grids, the accurate wind speed prediction (WSP) is critical to more efficient and reliable operations of power systems containing wind turbines. This paper presents a novel deep attention convolutional recurrent network with K-Shape and enhanced memory (DACRN-KM) for more accurate short-term WSP. To well capture spatial-temporal information among wind speeds measured across the wind farm, a DACRN is firstly developed to extract latent representations. Next, an auto-update memory module is developed to rebuild latent representations based on historical records. A K-shape clustering algorithm is applied to derive K patterns of rebuilt latent representations and the final prediction layer (FPL) is developed to generate the WSP result for latent representations assigned to one out of K patterns each time. The effectiveness of the proposed DACRN-KM is validated with data collected from 25 wind turbines (WTs) in multiple wind farms, in which a part of them is publicly accessible, by benchmarking against a set of the commonly considered and recently reported methods.

Research Area(s)

  • clustering analysis, Convolutional neural networks, Deep learning, Memory modules, multi-step prediction, neural networks, Wind farms, Wind power generation, Wind speed, Wind turbines

Citation Format(s)