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Forecasting Monthly Wind Energy Using an Alternative Machine Training Method with Curve Fitting and Temporal Error Extraction Algorithm

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  • Amir Abdul Majid

    (College of Engineering and Technology, University of Science and Technology of Fujairah, Fujairah P.O. Box 2202, United Arab Emirates)

Abstract

The aim of this research was to forecast monthly wind energy based on wind speed measurements that have been logged over a one-year period. The curve type fitting of five similar probability distribution functions (PDF, pdf), namely Weibull, Exponential, Rayleigh, Gamma, and Lognormal, were investigated for selecting the best machine learning (ML) trained ones since it is not always possible to choose one unique distribution function for describing all wind speed regimes. An ML procedural algorithm was proposed using a monthly forecast-error extraction method, in which the annual model is tested for each month, with the temporal errors between target and measured values being extracted. The error pattern of wind speed was analyzed with different error estimation methods, such as average, moving average, trend, and trained prediction, for adjusting the intended following month’s forecast. Consequently, an energy analysis was performed with effects due to probable variations in the selected Lognormal distribution parameters, according to their joint Gaussian probability function. Error estimation of the implemented method was carried out to predict its accuracy. A comparison procedure was performed and was found to be in line with the conducted Markov series analysis.

Suggested Citation

  • Amir Abdul Majid, 2022. "Forecasting Monthly Wind Energy Using an Alternative Machine Training Method with Curve Fitting and Temporal Error Extraction Algorithm," Energies, MDPI, vol. 15(22), pages 1-24, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8596-:d:974981
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    References listed on IDEAS

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    2. Shibo Li & Hu Zhou & Genzhu Xu, 2023. "Research on Optimal Configuration of Landscape Storage in Public Buildings Based on Improved NSGA-II," Sustainability, MDPI, vol. 15(2), pages 1-29, January.

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