针对电力负荷预测过程中普遍存在的负荷波动变化趋势明显、随机性强，以及预测模型的参数取值不合理导致的精度偏低问题，提出了一种基于ALIF-VMD（adaptive local iterative filtering - variational mode decomposition）二次分解和北方苍鹰优化算法（northern goshawk optimization, NGO）优化CNN-LSTM（convolutional neural networks - long short-term memory）的电力负荷组合预测模型，在使用交叉映射收敛方法（convergent cross-mapping, CCM）准确识别电力负荷的关键影响因素的基础上，创新性地联合使用ALIF、基于NGO的VMD和模糊熵（fuzzy entropy, FE）对原始负荷序列进行组合分解和必要的重组；针对分解和重组后生成的模态分量，结合NGO确定的CNN-LSTM模型最优超参数组合，建立预测精度高、训练时间短、收敛速度快的NGO-CNN-LSTM日前电力负荷组合预测模型。与其他基准模型的对比结果表明，该模型具有更好的适应性和预测精度，可为电力系统的安全、可靠、经济运行提供重要的技术支撑。

Aiming at obvious load fluctuation trend, strong randomness and low accuracy caused by unreasonable parameter values of the prediction model involved into the power load forecasting process, a combined prediction model composing of ALIF (adaptive local iterative filtering), VMD (variational mode decomposition), NGO (northern goshawk optimization) and CNN-LSTM (convolutional neural networks - long short-term memory) was established. Firstly, CCM (convergent cross-mapping) method was used to identify the key factors affecting the power load. Secondly, an innovative combination of ALIF, NGO-based VMD and FE (fuzzy entropy) was employed for combinatorial decomposition and necessary recombination of original load sequence. Next, based on the modal components generated after decomposition and recombination, combined with optimal hyperparameter combination of CNN-LSTM determined by NGO method, an NGO-CNN-LSTM day-ahead power load combination prediction model with the high prediction accuracy, short training time and fast convergence speed was formulated. Compared with other benchmark models, the obtained results demonstrated that the proposed model has the better adaptability and prediction accuracy, and can provide important technical support for the safe, reliable and economical operation of power system.