摘要
短期电力负荷数据具有复杂性和不确定性等特征,这些特征往往会对数据的预测结果产生不可控制的影响。使用传统的聚类方法对短期电力负荷数据进行聚类分析时,预测结果会因电力负荷的不确定性等特点产生偏差。此外,考虑到全局回归预测方法在建模阶段无法对不同部分的数据采用不同的建模方式,限制了对于不同分布区域或不同特征子集的自适应性能力的问题。文中采用K近邻和加权相似性的密度峰值聚类算法对短期电力负荷数据进行特征分类,并提出一种利用K近邻的局部加权线性回归模型对短期电力负荷进行预测。该模型的优点在于避免了欧氏距离对簇类中心选取的影响,降低了全局数据对局部数据的负面影响,避免了簇类划分的集中效应,提高了模型的泛化能力。通过与模糊C均值聚类和传统的全局回归预测方法对比,本文提出的模型对于真实电力数据的预测效果更加优越。
Short-term power load data are characterized by complexity and uncertainty,which often have an uncontrollable impact on the prediction results of the data.When short-term electricity load data are clustered and analyzed using traditional clustering methods,the prediction results will be biased due to characteristics such as the uncertainty of electricity load.In addition,considering that global regression forecasting methods are unable to use different modeling approaches for different parts of the data in the modeling stage,which limits the problem of adaptive capability for different distribution regions or different subsets of features.In this paper,we adopt the density peak clustering algorithm with K-nearest neighbor and weighted similarity to classify the features of short-term electricity load data,and propose a locally weighted linear regression model using K-nearest neighbor to forecast short-term electricity load.The advantages of this model are that it avoids the influence of Euclidean distance on the selection of cluster class centers,reduces the negative influence of global data on local data,avoids the centralized effect of cluster class division,and improves the generalization ability of the model.By comparing with fuzzy C-mean clustering and traditional global regression prediction methods,the model proposed in this paper is more superior for the prediction of real power data.
作者
王晨宇
张钊
侯佳龙
周红艳
陈雪波
WANG Chenyu;ZHANG Zhao;HOU Jialong;ZHOU Hongyan;CHEN Xuebo(School of Computer Science and Software Engineering,University of Science and Technology,Liaoning Anshan 114051;School of Electronic and Information Engineering,University of Science and Technology,Liaoning Anshan 114051)
出处
《东北电力大学学报》
2024年第4期113-120,共8页
Journal of Northeast Electric Power University
基金
国家自然科学基金项目(71771112)
流程工业综合自动化国家重点实验室开放课题基金资助项目(PAL-N201801)
辽宁省高校基本科研业务费专项资金。
关键词
密度峰值聚类
K近邻
局部加权线性回归
电力负荷预测
预测性能评价
density peak clustering
K nearest neighbor
locally weighted linear regression
power load forecasting
prediction performance evaluation
