基于生物免疫优化支持向量机算法的居民区负荷预测
作者:
作者单位:

(1.国网天津市电力公司电力科学研究院,天津 300384; 2.国网天津市电力公司城东供电分公司,天津 300250; 3.天津理工大学 机械工程学院,天津 300384)

作者简介:

田 禾 (1975-),女,博士,副教授,硕士生导师,从事综合能源利用、能源系统优化方面的研究。

通讯作者:

中图分类号:

基金项目:

国网天津市电力公司科技项目(KJ21-1-21)资助项目


Load prediction of residential areas based on biological immuno-optimized support vector machine algorithm
Author:
Affiliation:

(1.State Grid Tianjin Electric Power Research Institute, Tianjin 300384, China;2.State Grid Tianjin Chengdong Electric Power Supply Company, Tianjin 300250, China;3.School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China)

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    针对居民区用电负荷随机性强、稳定性差等问题,综合考虑各因素对居民用电负荷的影响,提出一种免疫支持向量机(support vector machine,SVM)算法负荷预测模型。以居民区历史用电量及相关气候数据为处理对象,使用PCA(principal component analysis)算法对电网历史数据进行处理,并结合免疫算法对电网历史数据进行预处理,形成数据簇并划定标签提供给预测模型进行训练。为提高模型精度,采用生物免疫优化算法对SVM模型参数进行优化,并在负荷预测环节,将预测误差作为调优依据,对预测模型进行反馈调优。将预测效果与常用于负荷预测的BP(back propagation)神经网络、SVM算法模型进行对比,免疫SVM算法负荷预测模型的短期、中期预测精准度均在98%以上,具有较好的精度与鲁棒性。

    Abstract:

    A prediction model for electric load based on an immune support vector machine (SVM) algorithm is proposed to address the issues of high randomness and poor stability in the electric load of residential areas.Considering various factors that affect the electric load of residents,the historical electric consumption and relevant climate data of residential areas are used as the processing objects.The principal component analysis (PCA) algorithm is utilized to preprocess the historical data of the power grid,and the immune algorithm is combined to preprocess the data by forming data clusters and defining labels for training the prediction model.To improve the accuracy of the model,the biological immune optimization algorithm is used to optimize the parameters of the SVM model.In the load prediction process,the prediction error is used as the basis for feedback tuning of the prediction model.The prediction performance of the immune SVM algorithm load prediction model is compared with that of the commonly used back propagation (BP) neural network and SVM algorithm model.The short-term and medium-term prediction accuracies of the immune SVM algorithm load prediction model are both above 98%,demonstrating good accuracy and robustness.