负荷功率模型的最优特征选择研究

Optimal feature selection of load power models

负荷功率呈现时空多样的变化特性,其影响因素众多,构建负荷功率模型的关键之一是确定模型的输入特征量。文中着重研究短期负荷功率模型的特征选择,旨在从历史负荷、气象、日期等众多特征中选出最优特征集。首先,采用最大信息系数、基于支持向量机的递归特征消除法和随机森林3种不同特征选择方法分别对输入特征集进行选择;然后,根据对比分析结果提出基于遗传算法的最优特征集搜索策略,选定XGBoost预测模型的误差指标作为适应性函数进行迭代优化搜索;最终,确定负荷功率模型的最优特征输入集。采用某地区220 kV变电站母线负荷数据进行算例分析,对比各方法所选特征集作为功率模型输入得到的负荷预测效果,验证了文中方法的有效性和准确性。

Load power fluctuation characteristics differ in time and space owing to many influencing factors.It is of great importance to determine the input features for load power modeling.This paper focuses on the feature selection of short-term power models.The purpose of this paper is to find the optimal set from the features including historical load,weather and date.Firstly,maximum information coefficient,recursive feature elimination method based on support vector machine and random forest are used for feature selection respectively.Secondly,the optimal feature set search strategy based on genetic algorithm is proposed according to the contrastive analysis.Finally,the optimal set of input features is finally determined.An example of calculation is carried out based on the bus load data of a 220 kV substation in a certain area.Compared with the load forecasting results of each feature selection method,the effectiveness and accuracy of the proposed feature selection method in short-term load forecasting have been verified.