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FOCUSED-Short-Term Wind Speed Forecast Correction Algorithm Based on Successive NWP Forecasts for Use in Traffic

Zdravko Kunić1,2, Bernard Ženko3, Biljana Mileva Boshkoska1,3

  • 1Faculty of Information Studies in Novo Mesto, 8000 Novo Mesto, Slovenia.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary

We developed FOCUSED (FOrecast Correction Using Successive prEDictions) to improve short-term wind speed forecasts, especially for dangerous bora gusts. This new algorithm enhances traffic safety by providing more accurate wind predictions.

Keywords:
forecast correctionneural networkssuccessive forecaststraffic managementwind speed prediction

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Area of Science:

  • Meteorology and atmospheric science
  • Artificial intelligence and machine learning
  • Transportation engineering

Background:

  • Short-term wind speed prediction is crucial for traffic management, particularly in coastal areas prone to hazardous bora gusts.
  • Current decision support systems exhibit occasional ambiguities in forecasting high-speed wind conditions, impacting traffic safety.
  • Accurate wind forecasting is essential for mitigating risks associated with extreme weather events in transportation.

Purpose of the Study:

  • To introduce and evaluate a novel algorithm, FOCUSED (FOrecast Correction Using Successive prEDictions), for enhancing short-term wind speed forecasts.
  • To improve the prediction accuracy of bora gusts, which pose significant risks to road traffic.
  • To provide a more reliable tool for traffic management systems operating in challenging coastal wind conditions.

Main Methods:

  • The FOCUSED algorithm iteratively forecasts wind speed multiple times for the same future period using numerical weather prediction model characteristics.
  • Iterative predictions serve as input features for the FOCUSED algorithm to generate corrected wind speed forecasts.
  • Performance evaluation involved comparing FOCUSED against artificial neural networks, random forests, support vector machines, and linear regression.

Main Results:

  • The FOCUSED algorithm demonstrated superior performance in wind speed forecast correction compared to traditional machine learning methods.
  • The study utilized a comprehensive dataset including five years of real-world wind measurements from the Krk bridge, Croatia.
  • Complementary historical forecasts from the ALADIN (Aire Limitée Adaptation dynamique Développement InterNational) numerical weather prediction model were incorporated.

Conclusions:

  • FOCUSED offers a significant improvement in short-term wind speed forecasting accuracy, particularly for extreme events like bora gusts.
  • The algorithm's iterative prediction approach effectively corrects forecast errors, enhancing its practical applicability.
  • The findings suggest FOCUSED can substantially improve the reliability of traffic management systems in hazardous wind conditions.