高维稀疏电力负荷数据无监督挖掘算法OA

[Objective]In power systems,load data analysis is crucial for power grid dispatching,planning,and management.However,with the deepening of the complexity and intelligence of power systems,power load data exhibit characteristics of high dimensionality and sparsity,which poses significant challenges to traditional data analysis methods in terms of processing efficiency and ability to capture the intrinsic information of load changes.An efficient unsupervised data mining algorithm was proposed in this paper,which was aimed at improving the processing efficiency and information extraction capability of high-dimensional sparse power load data.[Methods]Firstly,a feature ranking method based on information entropy was adopted to determine feature importance.Data initialization was completed by calculating mutual information and conducting centralization and standardization.Features with the maximum mutual information were selected to expand the feature set,and feature subsets were screened by calculating relevant information entropy.The subset screening process was optimized using support vector machine(SVM)classifier as the benchmark model,and an improved particle swarm optimization algorithm was introduced for secondary feature selection.Meanwhile,the SVM classifier was used to complete the preliminary feature screening.Secondly,the principal component analysis(PCA)was introduced for dimensionality reduction.The sample matrix was centralized,and the covariance matrix was established.Eigenvalues and eigenvectors were obtained,and eigenvectors were selected to construct a new matrix to achieve dimensionality reduction.Finally,an autoencoder network based on unsupervised learning was introduced to conduct unsupervised mining.In the encoding stage,input data were converted into feature representations.In the decoding stage,data recovery was completed.Through steps such as data setting,clustering execution,data point screening,data balancing processing,and model training to obtain a classification interface,hidden feature extraction and network adjustment were realized.[Results]When the algorithm in this paper is applied,the Rand index values all exceed 0.60,indicating high clustering accuracy.In 60 iterations of experiments,the maximum memory overhead ratio is about 8.3%,demonstrating the algorithm's high efficiency in computing resource utilization.Compared with other traditional methods,this algorithm can achieve higher processing efficiency and better mining results when dealing with high-dimensional sparse power load data.[Conclusions]The unsupervised mining algorithm performs excellently in the analysis of high-dimensional sparse power load data.By reducing computational complexity through feature selection and dimensionality reduction,and mining nonlinear features with the autoencoder network,it significantly improves the accuracy and efficiency of data mining,and has strong applicability and feasibility.Its innovation lies in integrating multiple methods such as information entropy-based feature ranking,SVM,improved particle swarm optimization,PCA,and autoencoder network to form a complete system from feature processing to data mining.This system can not only effectively address the challenges in mining high-dimensional sparse power load data but also provide a new and effective means for load data analysis in power systems,which is of great significance for promoting the intelligent development of power systems.