电力负荷具有时空多变的特性，受众多因素的影响，在短期负荷预测中较多的输入特征会造成维度灾难，导致模型预测性能不佳，因此选择合理的输入特征集至关重要。文章提出一种新的短期负荷预测特征选择方法——mRMR-IPSO双阶段法。利用最大相关最小冗余（max-relevance and min-redundancy，mRMR）判据对原始特征进行排序，考虑输入特征与输出特征之间相关性和输入特征间冗余性，筛选掉一些排序靠后的特征，初选出对预测效果影响显著的特征子集；采用基于改进的粒子群优化算法（improved particle swarm optimization，IPSO）的搜索策略，以LightGBM模型的预测精度为适应度函数，对初选特征子集进行精选，得到最优特征子集。算例结果表明，所提方法能在对原始特征集大幅降维的情况下提升预测精度。

Power load exhibits characteristics of temporal and spatial variation and is affected by various factors. In short-term load forecasting, an excessive number of input features can cause dimensionality disasters and lead to poor model prediction performance. Therefore, selecting a reasonable input feature set is crucial. This article proposes a novel feature selection method for short-term load forecasting–the mRMR-IPSO two-stage method. The max-relevance and min-redundancy (mRMR) criterion is employed to rank the original features, considering both the correlation between input and output features and the redundancy among input features. This process filters out less impactful features ranked lower and initially selects these significantly influencing the prediction. Then, an improved particle swarm optimization (IPSO) algorithm-based search strategy is adopted. The prediction accuracy of the LightGBM model is used as the fitness function during the search, facilitating the selection of primary feature subsets and obtaining optimal feature subsets. Calculation examples show that the proposed method improves prediction accuracy while substantially reducing the original feature set.

TM715

国网湖北电力公司科技项目(521520220006)。