Short-Term Wind Power Prediction Based on Feature Crossover Mechanism and Error Compensation

At present, wind power forecasting methods mainly include physical modeling, statistical modeling and artificial intelligence algorithm modeling. The traditional physical modeling and statistical modeling methods are difficult to collect data and select parameters, and have weak processing ability for a large number of data, so it is difficult to establish an accurate prediction model. Therefore, in practical applications, artificial intelligence algorithms are usually used to predict wind power. However, the current research on wind power prediction focuses on the use and improvement of artificial intelligence algorithms, and does not take into account the correlation between different features in the data and wind power, as well as the difference in the size of correlation. The model trained on this basis can only establish a single, superficial correlation, and cannotmine deeper relationships, which is not conducive to short-term prediction of wind power. Therefore, this paper first introduces the feature crossover mechanism, analyzes the correlation of data features and cross combines them, increases feature dimensions, strengthens the learning ability of the algorithm for nonlinear features and deep hidden associations, and forms the FC-CNN-LSTM prediction model based on CNN-LSTM network improvement. Then, use the error value generated by the prediction model in the prediction as the training data, train and generate the error compensation model, and use the data generated by the error compensation model to compensate the wind power prediction data, so as to further improve the prediction accuracy. Finally, through the measured data of a wind farm, it is verified that the FC-CNN-LSTM model has a higher prediction accuracy, and after the error compensation process is added, it can further reduce the error compared with the traditional prediction methods, which has significant advantages. The simulation results of the actual data in a region show that: (1) the feature crossing mechanism can effectively improve the learning ability of the model for nonlinear features and deep hidden associations, thus improving the prediction accuracy of the model. The prediction accuracy of the FC-CNN-LSTM model is 14.3% higher than that of the CNN-LSTM model; (2) The error compensation model based on FC-CNN-LSTM model can accurately predict the error of power prediction model, greatly reducing the prediction error, and greatly improving the prediction accuracy after compensation by 46.5% compared with the FC-CNN-LSTM model before compensation; Finally, the following conclusions are drawn through analysis: (1) Compared with the CNN-LSTM model, the FC-CNN-LSTM model proposed in this paper has obvious advantages in the accuracy of the prediction of the ultra short termminute level wind power. It can more keenly capture the subtle changes of the characteristics related to the wind power, adapt to the rapid changes of meteorological factors, and more accurately predict the wind power, which is more suitable for practical engineering projects; (2) The error compensation mechanism based on FC-CNN-LSTM model proposed in this paper can further improve the accuracy of wind power prediction, and can be used in different application scenarios and with different algorithms, with high adaptability; (3) In addition, the disadvantage of the FC-CNN-LSTM model is that when the wind power is small and close to zero, the wind speed decreases significantly, and the contribution of other weak correlation features in the model will be relatively improved. Although these weak correlation features are related to the wind power to some extent, they do not occupy the same dominant position as the wind speed. Therefore, when the wind speed decreases significantly, the FC-CNN-LSTM model improves the learning of weak correlation features, The prediction accuracy will be worse than that of the CNN-LSTM model, which can be solved by setting a limit on the prediction results according to the wind speed. © 2023 Chinese Machine Press. All rights reserved.