Wavelet decomposition and neural networks: a potent combination for short term wind speed and power forecasting

Adaiyibo E. Kio, Jin Xu, Natarajan Gautam, Yu Ding

Research output: Contribution to journalArticlepeer-review

Abstract

The forecast of wind speed and the power produced from wind farms has been a challenge for a long time and continues to be so. This work introduces a method that we label as Wavelet Decomposition-Neural Networks (WDNN) that combines wavelet decomposition principles and deep learning. By merging the strengths of signal processing and machine learning, this approach aims to address the aforementioned challenge. Treating wind speed and power as signals, the wavelet decomposition part of the model transforms these inputs, as appropriate, into a set of features that the neural network part of the model can ingest to output accurate forecasts. WDNN is unconstrained by the shape, layout, or number of turbines in a wind farm. We test our WDNN methods using three large datasets, with multiple years of data and hundreds of turbines, and compare it against other state-of-the-art methods. It’s very short-term forecast, like 1-h ahead, can outperform some deep learning models by as much as 30%. This shows that wavelet decomposition and neural network are a potent combination for advancing the quality of short-term wind forecasting.

Original languageEnglish (US)
Article number1277464
JournalFrontiers in Energy Research
Volume12
DOIs
StatePublished - 2024
Externally publishedYes

Keywords

  • deep learning
  • machine learning
  • power forecasting
  • spatio-temporal
  • wavelet decomposition
  • wind farm

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Economics and Econometrics

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