Noise-intensification data augmented machine learning for day-ahead wind power forecast

Hao Chen*, Yngve Birkelund, Bjørn Morten Batalden, Abbas Barabadi

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)
6 Downloads (Pure)


The day-ahead wind power forecast is essential for the designation of dispatch schedules for the grid and rational arrangement for production planning by power generation companies. This paper specifically investigates the effect of adding noise to the original wind data for forecasting models. Linear regression, artificial neural networks, and adaptive boosting predictive models based on data-intensification white noise and uniform noise are evaluated in detail and their superiority over the original data-based models is compared. The results demonstrate that solely injecting noise into the dataset can statistically boost the performance of all forecasting models with learning algorithms. The findings of this study suggest a fresh perspective for developing wind power prediction models and carry certain wind energy engineering merits.

Original languageEnglish
Pages (from-to)916-922
Number of pages7
JournalEnergy Reports
Issue numberSuppl. 10
Publication statusPublished - Nov 2022
Externally publishedYes


  • Data science
  • Machine learning
  • Noise
  • Power forecast
  • Statistical inference
  • Wind energy


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