Elman neural network considering dynamic time delay estimation for short-term forecasting of offshore wind power

Huang, Jing; Qin, Rui

doi:10.1016/j.apenergy.2024.122671

Cited by 4 publications

(1 citation statement)

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“…These algorithms can learn from historical data the influence of various factors such as wind speed, wind direction, temperature, and air pressure on the wind power output, thereby improving the accuracy of prediction. Huang and Qin [6] proposes a short-term offshore wind power prediction method that considers dynamic time-delay effects to intuitively capture power prediction information. Based on the nonlinear coupling relationship, dynamic sliding windows matching different mean periods are introduced.…”

Section: Related Workmentioning

confidence: 99%

Research on the Short-Term Prediction of Offshore Wind Power Based on Unit Classification

Zhang,

Liu,

Yan

2024

Electronics

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The traditional power prediction methods cannot fully take into account the differences and similarities between units. In the face of the complex and changeable sea climate, the strong coupling effect of atmospheric circulation, ocean current movement, and wave fluctuation, the characteristics of wind processes under different incoming currents and different weather are very different, and the spatio-temporal correlation law of offshore wind processes is highly complex, which leads to traditional power prediction not being able to accurately predict the short-term power of offshore wind farms. Therefore, aiming at the characteristics and complexity of offshore wind power, this paper proposes an innovative short-term power prediction method for offshore wind farms based on a Gaussian mixture model (GMM). This method considers the correlation between units according to the characteristics of the measured data of units, and it divides units with high correlation into a category. The Bayesian information criterion (BIC) and contour coefficient method (SC) were used to obtain the optimal number of groups. The average intra-group correlation coefficient (AICC) was used to evaluate the reliability of measurements for the same quantized feature to select the representative units for each classification. Practical examples show that the short-term power prediction accuracy of the model after unit classification is 2.12% and 1.1% higher than that without group processing, and the mean square error and average absolute error of the short-term power prediction accuracy are reduced, respectively, which provides a basis for the optimization of prediction accuracy and economic operation of offshore wind farms.

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Section: Related Workmentioning

confidence: 99%