Abstract:

For stable operation of the power system and to meet its demand for short-term power load forecasting accuracy, a short-term power load forecasting method based on the improved Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) model is proposed. A Kernel Principal Component Analysis (KPCA) is used to process the multidimensional input data, and the primary influencing factors are effectively extracted as inputs for the subsequent prediction model. A CNN-GRU combination model with an improved Osprey Optimization Algorithm (OOA) is constructed for training and prediction, and an attention mechanism is introduced to strengthen the influence of important information for enhancing the prediction performance of the prediction model. Finally, the eXtreme Gradient Boosting (XGBoost) algorithm optimized by Bayesian Hyperparameters (BH) theory is used to optimize the prediction error, a simulation model is constructed for comparison with multiple models, and the effectiveness of the proposed method is verified based on the obtained prediction effect curves and various performance indexes. The experimental results show that the Mean Absolute Percentage Error (MAPE) of the proposed CNN-GRU model during training and testing are 1.56% and 1.99%, respectively, indicating that the proposed model has improved prediction accuracy.

Key words: short-term load forecasting, Kernel Principal Component Analysis (KPCA), Osprey Optimization Algorithm (OOA), Gated Recurrent Unit (GRU), eXtreme Gradient Boosting (XGBoost)

摘要：

为维护电力系统的稳定运行, 满足电力系统对于短期电力负荷预测精度的需求, 提出一种基于改进卷积神经网络与门控循环单元(CNN-GRU)模型的短期电力负荷预测方法。采用核主成分分析(KPCA)法处理多维输入数据, 提取主要影响因素作为后续预测模型的输入。构建以改进鱼鹰算法(OOA)优化的CNN-GRU组合模型进行训练和预测, 并引入注意力机制加强重要信息的影响, 提升预测模型的预测性能。最后采用贝叶斯超参数(BH)理论优化的极端梯度提升(XGBoost)模型优化预测误差, 并搭建仿真模型与多个模型进行对比实验, 根据所得到的预测效果曲线和各项性能指标验证所提方法的有效性。实验结果表明, 提出的改进CNN-GRU模型在训练与测试时的平均绝对百分比误差(MAPE)分别为1.56%和1.99%, 由此可以得出所提出的改进预测模型具有更好的预测精度。

关键词: 短期负荷预测, 核主成分分析, 鱼鹰优化算法, 门控循环单元, 极端梯度提升