风电机组具有结构复杂，运维困难，且长期处于恶劣的工作环境的特点。风电备件的需求预测有助于为风电场配备最合适的备件数，以确保风电场的平稳、高效运行。构建主成分分析-反向传播（principal component analysis-back propagation, PCA-BP）模型，针对受多因素影响的复杂备件，先利用PCA将影响风电备件的要素进行筛选，再利用BP神经网络算法，得到最为精确的预测结果。比较自回归积分滑动平均（autoregressive integrated moving average, ARIMA）模型、BP神经网络预测和PCA-BP神经网络预测的结果。结果表明：PCA能显著降低神经网络预测误差，预测的精度为93.94%，高于BP神经网络预测的88.39%和ARIMA模型的85.31%，所以PCA-BP神经网络模型的预测精度准确且有可靠结果，能够适用于风机备件的需求预测。

Wind turbines are characterised by complex structures, difficult operation and maintenance, and long-term exposure to harsh operating environments. The demand prediction of wind turbine spare parts helps to equip wind farms with the most suitable number of spare parts to ensure the smooth and efficient operation of wind farms. Principal component analysis-back propagation (PCA-BP) model was constructed for the complex spare parts affected by multiple factors, and the elements affecting the wind power spare parts were first screened by PCA, and then the BP neural network algorithm was used to obtain the most accurate prediction results. The results of autoregressive integrated moving average (ARIMA) model, BP neural network prediction and PCA-BP neural network prediction were compared. It shows that PCA can significantly reduce the neural network prediction error, and the accuracy of the prediction is 93.94%, which is higher than the 88.39% of the BP neural network prediction and the 85.31% of the ARIMA model, so the PCA-BP neural network model has accurate prediction accuracy and reliable results, and it can be applied to the wind turbine spare parts demand prediction.