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Short-Term Wind Speed Forecasting Using a Multi-model Ensemble

  • Chi Zhang
  • Haikun Wei
  • Tianhong Liu
  • Tingting Zhu
  • Kanjian Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9377)

Abstract

Reliable and accurate short-term wind speed forecasting is of great importance for secure power system operations. In this study, a novel two-step method to construct a multi-model ensemble, which consists of linear regression, multi-layer perceptrons and support vector machines, is proposed. The ensemble members first compete with each other in a number of training rounds, and the one with the best forecasting accuracy in each round is recorded. Then, after all the training rounds, the occurrence frequency of each member is calculated and used as the weight to form the final multi-model ensemble. The effectiveness of the proposed multi-model ensemble has been assessed on the real datasets collected from three wind farms in China. The experimental results indicate that the proposed ensemble is capable of providing better performance than the single predictive models composing it.

Keywords

Wind speed forecasting Model combination Ensemble Linear regression Multi-layer perceptron Support vector machine 

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  • Chi Zhang
    • 1
  • Haikun Wei
    • 1
  • Tianhong Liu
    • 1
  • Tingting Zhu
    • 1
  • Kanjian Zhang
    • 1
  1. 1.Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of AutomationSoutheast UniversityNanjingP.R. China

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