An intelligent hybrid method based on Monte Carlo simulation for short-term probabilistic wind power prediction

Ali Akbar Abdoos, Hatef Abdoos, Javad Kazemitabar, Mohammad Mehdi Mobashsher and Hooman Khaloo

Energy, 2023, vol. 278, issue PA

Abstract: Precise estimation of wind power is exceedingly challenging due to the quick and random fluctuations in wind speed. Therefore, this article presents a probabilistic intelligent method for wind power prediction to minimize the risk caused by the uncertainty of the generated power. The proposed method is realized through four important steps: 1. Analyzing the wind power time series signals; 2. Creating training patterns; 3. Training patterns by applying machine learning; 4. Making a probabilistic prediction based on the Monte-Carlo simulation. To estimate the wind power more accurately, the time series of historical data on wind power are decomposed into different levels by appropriate signal analysis tools, i.e., the wavelet transform and variational mode decomposition. Then, the intelligent tools, i.e., extreme learning machine and support vector regression, are used as predictor cores due to their high speed and accuracy. The obtained results show that the presented method can accurately predict the generated wind power in 10-min intervals for the next hour. Moreover, the average value of the estimated data for 1000 repetition steps in probabilistic protection is very close to the real data. Thus, the proposed method can be effectively applied to both deterministic and probabilistic predictions.

Keywords: Probabilistic prediction; Wind power; Artificial intelligence; Monte Carlo simulation (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:278:y:2023:i:pa:s0360544223013087

DOI: 10.1016/j.energy.2023.127914

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