Mining and Clustering Techniques for Load Operation Characteristics of the Internet of Things

In the era of the Internet of Things (IoT), load management has become one of the important challenges in the energy field. Traditional load management methods have high requirements for data accuracy and real-time performance, as well as difficulties in dealing with diverse load characteristics. Therefore, it is particularly urgent to seek a method that can more effectively identify and manage load characteristics. This article proposes a load management method based on clustering technology to address the problems of traditional methods. This method includes steps such as data preprocessing, feature selection, clustering analysis, and interpretation of clustering results. By effectively classifying and managing load data, it is possible to achieve better utilization of energy and improvement of system efficiency. The purpose of this study is to explore a load management method suitable for the Internet of Things environment. Through the application of clustering technology, accurate identification and management of load characteristics have been achieved. The proposal of this method provides new ideas for load management in the energy field, and has important theoretical and practical significance. The experimental results have demonstrated that the CNN algorithm has a longer training time (32.75 seconds) for clustering analysis. The training time of the K-mean algorithm is 13 seconds. The K-means algorithm has a relatively short training time for clustering analysis.