电网惯量大小衡量了系统的频率稳定性，提前对系统惯量水平进行准确预测可以避免因惯量低而造成风险。为此，提出了一种基于模态分解及特征融合多算法混合神经网络模型对系统等效惯量进行短期预测。首先，采用改进自适应噪声完备集合经验模态分解（improved complete ensemble empirical mode decomposition with adaptive noise，ICEEMDAN）方法对四季惯量分解处理，依据各分解分量的精细复合多尺度模糊熵（refined composite multi-scale fuzzy entropy，RCMFE）重构得到新序列。其次，采用最小冗余最大相关性（minimum redundancy maximum relevance，mRMR）方法衡量不同分解分量与不同特征之间的相关性，筛选出高相关、低冗余特征子集。最后使用基于贝叶斯优化算法的双向长短期记忆网络（bidirectional long-short-term memory，BiLSTM）模型对不同季节惯量的不同分量进行预测，累加得到最终预测结果。通过选用国内外典型算例进行试验，验证了所提方法能够有效平衡预测精度与预测时间，解决了因季节性差异对系统惯量预测结果造成影响的问题。

The grid inertia magnitude measures the frequency stability of the system, and accurate prediction of the system inertia level in advance can avoid the risk caused by low inertia. To this end, a multi-algorithm hybrid neural network model based on modal decomposition and feature fusion for short-term prediction of system equivalent inertia is proposed. Firstly, an improved complete ensemble empirical mode decomposition with adaptive noise is used to decompose the inertia of four seasons, and a new sequence is obtained by reconstructing the new inertia based on the fine composite multi-scale fuzzy entropy of each decomposed component. Secondly, the minimum redundancy maximum relevance method is used to measure the correlation between different decomposition components and different features, and a subset of highly correlated and low redundancy features is filtered out. Finally, a bidirectional long-short-term memory network model based on Bayesian optimization algorithm is used to predict different components of different seasonal inertias, and the final prediction results are accumulated. Typical examples at home and abroad are selected for testing, which verify that the proposed method can effectively balance the prediction accuracy and prediction time, and solves the problem of seasonal differences affecting the prediction results of system inertia.