基于新型日期映射法和ISGU混合模型的短期电力负荷预测

针对电力负荷非线性、预测条件多样性、预测模型参数设置主观性等问题，提出一种基于强适应性的日均负荷日期映射法、高非线性拟合性能的门控循环单元(Gate Recurrent Unit, GRU)和强搜索性能的改进麻雀搜索算法(Improved Sparrow Search Algorithm, ISSA)相结合的ISSA-GRU(ISGU)混合模型进行短期电力负荷预测(Short-term Load Forecasting, STLF)。首先，利用日均负荷日期映射法对星期-节假日因素进行映射，解决该因素因非数字化导致不易输入预测网络的问题。随后，从诸多相关因素中筛选出高度相关特征值，以此解决预测条件多样性问题。最后，构建GRU网络进行负荷预测，并引入ISSA算法对GRU网络参数进行客观配置。为验证ISGU混合模型的有效性，采用新加坡电力负荷数据进行实验，并将实验结果与现有算法进行比较。实验结果表明，所提方法对STLF具有良好性能，有效提高了STLF统计标准的精度指标。

Short-term power load forecasting based on a new date mapping method and an ISGU hybrid model

Given the nonlinear power load, diversity of prediction conditions, subjectivity of parameter setting of the prediction model, etc., an ISSA-GRU (ISGU) hybrid model based on the combination of a date mapping method based on daily average load with strong adaptability, a Gate Recurrent Unit (GRU) with high non-linear fitting performance and an improved sparrow search algorithm (ISSA) with strong search ability are proposed for short-term load forecasting (STLF). First, the data mapping method based on daily average load is used to map the week-holiday factor to solve the problem that it is difficult to input the prediction network because of non-digitization. Then, highly relevant eigenvalues are selected from many correlated factors to deal with the diversity of prediction conditions. Finally, the GRU network is constructed for load forecasting, and the ISSA algorithm is used to configure GRU network parameters objectively. To verify the effectiveness of the ISGU hybrid model, we use the Singapore power load data experiment, and compare the experimental results with the existing algorithms. The results show that this method has good performance for STLF and effectively improves the accuracy of STLF statistical standards. This work is supported by the National Natural Science Foundation of China (No. 51977039).