A Double-stage Hierarchical Hybrid PSO-ANFIS Model for Short-term Wind Power Forecasting

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

*Corresponding author for this work

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

Abstract

Assessment of the output power 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 based adaptive neuro-fuzzy inference system (double-stage hybrid PSO-ANFIS) for short-term wind power prediction of a microgrid wind farm in Beijing, China. The approach has two hierarchical stages. The first PSO-ANFIS 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 influence of input data dependency on prediction accuracy has been analyzed by dividing the input data into five subsets. The proposed approach has attained significant prediction accuracy improvements. The performance of the proposed model is compared with five other prediction approaches and showed the best accuracy improvement of all.

Original languageEnglish
Title of host publicationProceedings of the Ninth Annual IEEE Green Technologies Conference, GreenTech 2017
PublisherIEEE
Pages342-349
Number of pages8
DOIs
Publication statusPublished - 2017
MoE publication typeA4 Article in a conference publication
EventIEEE Green Technologies Conference - Denver, United States
Duration: 29 Mar 201731 Mar 2017
Conference number: 9

Publication series

NameIEEE Green Technologies Conference
PublisherIEEE
ISSN (Print)2166-546X
ISSN (Electronic)2166-5478

Conference

ConferenceIEEE Green Technologies Conference
Abbreviated titleGreenTech
CountryUnited States
CityDenver
Period29/03/201731/03/2017

Keywords

  • Forecasting
  • Fuzzy logic
  • Neural network
  • Numerical weather prediction
  • Particle swarm optimization
  • Wind power
  • Speed prediction

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