A Double-Stage Hierarchical Hybrid PSO-ANN Model for Short-Term Wind Power Prediction

Abinet Tesfaye Eseye*, Jianhua Zhang, Dehua Zheng, Han Li, Gan Jingfu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

Power output of wind generators is always associated with some uncertainties due to wind speed and other weather parameters alteration, and precise short-term forecasts are essential for their efficient operation. This can efficiently support transmission and distribution system operators and schedulers to improve the power network control and management. In this paper, we propose a double stage hierarchical particle swarm optimization trained artificial neural network (double-stage hybrid PS-ANN) model for short-term wind power prediction of a microgrid wind farm in Beijing, China. The model has two hierarchical stages. The first PS-ANN stage employs numerical weather prediction (NWP) meteorological parameters to forecast wind speed at the wind farm exact site and turbine hub height. The second stage models the actual wind speed and power relationships. Then, the predicted next day's wind speed by the first stage is applied to the second stage to forecast next day's wind power. The proposed approach has attained significant prediction accuracy improvements. The performance of the proposed model is compared with other two prediction approaches and showed best accuracy improvement than both methods.

Original languageEnglish
Title of host publicationProceedings of the IEEE 2nd International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2017
PublisherIEEE
Pages489-493
Number of pages5
ISBN (Electronic)978-1-5090-4499-3
DOIs
Publication statusPublished - 2017
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Cloud Computing and Big Data Analysis - Chengdu, China
Duration: 28 Apr 201730 Apr 2017
Conference number: 2

Conference

ConferenceIEEE International Conference on Cloud Computing and Big Data Analysis
Abbreviated titleICCCBDA
CountryChina
CityChengdu
Period28/04/201730/04/2017

Keywords

  • Artificial neural network
  • Numerical weather prediction
  • Particle swarm optimization
  • Prediction
  • Wind power
  • Spatial correlation
  • Speed prediction
  • Neural-networks

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