基于特征选取与TSO-BP短期电力负荷预测研究  

Research on the Short-termed Power Load Prediction Based on the Feature Selection and TSO-BP

作　　者：高昕[1] 郑前东 GAO Xin;ZHENG Qiandong(School of Electrical and Information Engineering,Anhui University of Science and Technology,Huainan Anhui 232001,China)

机构地区：[1]安徽理工大学电气与信息工程学院,安徽淮南232001

出　　处：《安徽理工大学学报(自然科学版)》2025年第1期57-63,共7页Journal of Anhui University of Science and Technology:Natural Science

基　　金：安徽理工大学博士基金资助项目(111127)。

摘　　要：目的为降低环境因素对电力负荷预测的影响以及提高短期负荷预测精度。方法提出一种结合皮尔逊相关系数(PCC)、主成分分析(PCA)、金枪鱼群优化算法(TSO)改进BP神经网络的短期电力负荷预测模型。首先,为消除无关变量的影响,利用PCC进行特征选取,挑选出与负荷预测有关的气象属性;其次,利用PCA提取气象特征序列中的关键影响因子,消除原始序列的相关性和冗余性,降低模型输入维度,提高训练效率;最后,为解决传统BP神经网络在初始权重和阈值参数上具有随机性的问题,采用TSO来搜寻最优解代替随机参数,获得改进的模型。结果利用某一地区的电力负荷数据进行仿真分析,结果表明所构建模型预测平均绝对百分比误差达到了0.52%。结论证明了经特征选取与TSO优化后模型具有更高的预测精度。Objective In order to reduce the influence of environmental factors on the power load forecasting and improve the accuracy of short-term load forecasting.Methods In this paper,a short-termed power load prediction model based on Pearson correlation coefficient(PCC)、principal component analysis(PCA)and the tuna optimization algorithm(TSO)to improve BP neural network was proposed.Firstly,to eliminate the influence of irrelevant variables,PCC was used to select features and select meteorological attributes related to load forecasting.Secondly,the key influencing factors in the meteorological feature sequence were extracted with the use of PCA to eliminate the correlation and redundancy of the original sequence,reduce the input dimension of the model and improve the training efficiency.Finally,the improved model was obtained by using TSO to search for the optimal solution instead of the random parameters,in order to solve the problem of randomness in the initial weights and threshold parameters of the traditional BP neural network and.Results The average absolute error percentage of the constructed model reached 0.52%when the simulation analysis was carried out with the use of the power load data of a certain region.Conclusion The model has higher prediction accuracy after the feature selection and TSO optimization.

关 键 词：特征选取 电力负荷预测 金枪鱼群算法 最优参数 

分 类 号：TM715[电气工程—电力系统及自动化]