Short-Term Wind Power Forecasting Using a Double-Stage Hierarchical Hybrid GA-ANFIS Approach

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

Wind generation power output estimation is always associated with some uncertainties as a result of wind speed and other weather parameters intermittency, and accurate short-term predictions are important for their efficient operation. This can greatly help transmission and distribution system operators and schedulers to improve the power network control and management. In this paper, a double stage hierarchical genetic algorithm based adaptive neuro-fuzzy inference system (double-stage hybrid GA-ANFIS) approach is proposed for short-term wind power forecast of a microgrid wind farm in Beijing, China. The approach has two hierarchical stages. The first GA-ANFIS stage utilizes numerical weather prediction (NWP) meteorological parameters to predict wind speed at the wind farm exact site and turbine hub height. The second stage maps the actual wind speed and power relationships. Then, the forecasted next day's wind speed by the first stage is applied to the second stage to predict next day's wind power. The presented approach has achieved considerable prediction accuracy enhancement. The accuracy of the proposed model is compared with other four forecasting methods and resulted in the best accuracy improvement of all.

Original languageEnglish
Title of host publicationProceedings of the 2nd International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2017
PublisherIEEE
Pages499-503
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

  • Forecasting
  • Fuzzy logic
  • Genetic algorithm
  • Neural network
  • Numerical weather prediction
  • Wind power
  • Spatial correlation
  • Speed prediction

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