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

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. •Simultaneous implementation of the deterministic and probabilistic wind power predictions.•A few adjustable parameters of the proposed method make its implementation simple.•Using only wind power time series of historical data without the need for weather parameters.•Prediction of rapid changes in wind power by analyzing the time series signals using powerful VMD transformations.•High speed of the prediction process using ELM as a fast machine learning technique.