电网的可靠运行及持续发展离不开对短期电力负荷的高效、准确预测。针对表征电网负荷变化的历史数据具有复杂性和时序性等特点，且现有的机器学习预测方法仍存在依据经验选取关键参数的不足，利用卷积神经网络（CNN）提取表征负荷变化的多维特征向量，构造成时间序列输入到门控循环单元(GRU)，并使用改进麻雀搜索算法（ISSA）对GRU网络中的超参数进行迭代寻优。预测试验样本来自云南某地区的负荷数据，所提方法的预测精度达到了98.624%，与循环神经网络(RNN)、GRU和长短期记忆(LSTM)等神经网络预测方法进行对比，算例表明，所提方法克服了依据经验选取关键参数难题的同时具有更高的预测精度。

Efficient and accurate shortterm power load forecasting plays an important role in the reliable operation and sustainable development of the power grid. In view of the complexity and timing characteristics of the factors that affect the changes in power grid load, and the shortcomings of the existing machine learning prediction methods in selecting key parameters based on experience, the convolutional neural network (CNN) is used to extract the multidimensional feature vector representing the load change, which is constructed as a time series and input to the gated recurrent unit (GRU), and the improved sparrow search algorithm (ISSA) is used to iteratively optimize the hyperparameters in the GRU network. The prediction experimental sample comes from the load data of a certain area in Yunnan, and the prediction accuracy of the proposed method reaches 98.624%. Compared with the neural network prediction methods of RNN, GRU and LSTM, the calculation example shows that the proposed method overcomes the difficulty of selecting key parameters based on experience and has higher prediction accuracy.

TM714

云南省教育厅科学研究基金项目（2022J1279）