电力系统自动化2024，Vol.48Issue(7)：248-257,10.DOI:10.7500/AEPS20230926006
基于低风速功率修正和损失函数改进的超短期风电功率预测
Ultra-short-term Wind Power Prediction Based on Power Correction Under Low Wind Speed and Improved Loss Function
摘要
Abstract
Wind power has strong fluctuation and randomness. In order to further improve the prediction accuracy of wind power, an ultra-short-term wind power prediction model based on power correction under low wind speed and an improved loss function is proposed. The model uses convolutional neural networks, self-attention mechanisms and bidirectional gated recurrent unit to capture long-term temporal dependencies of wind power sequences. In order to solve the problem that it is difficult for the neural network to accurately fit the waiting wind state under low wind speed, the model corrects the predicted power under low wind speed by predicting wind speed and combining wind power at the current period. To solve the stability problem of parameter training, the model introduces a multivariate nonlinear loss function to extract the correlation between sequences by improving the prediction strategy and shared weights. The results show that the proposed model is superior to the comparison model in many error indices, and can effectively improve the effect of the ultra-short-term wind power prediction.关键词
超短期风电功率预测/功率修正/损失函数改进/神经网络模型Key words
ultra-short-term wind power prediction/power correction/loss function improvement/neural network model引用本文复制引用
臧海祥,赵勇凯,张越,程礼临,卫志农,秦雪妮..基于低风速功率修正和损失函数改进的超短期风电功率预测[J].电力系统自动化,2024,48(7):248-257,10.基金项目
国家自然科学基金资助项目(52077062). This work is supported by National Natural Science Foundation of China(No.52077062). （52077062）
