Abstract
In view of the limitation of “hard assignment” of clusters in traditional clustering methods and the difficulty of meeting the requirements of clustering efficiency and clustering accuracy simultaneously in regard to massive data sets, a load classification method based on a Gaussian mixture model combining clustering and principal component analysis is proposed. The load data are fed into a Gaussian mixture model clustering algorithm after principal component analysis and dimensionality reduction to achieve classification of large-scale load datasets. The method in this paper is used to classify loads in the Canadian AMPds2 public dataset and is compared with K-Means, Gaussian mixed model clustering and other methods. The results show that the proposed method can not only achieve load classification more effectively and finely, but also save computational cost and improve computational efficiency.
Highlights
Load classification refers to the processing of load data from a large number of power devices to extract typical load profiles [1], which can be applied to electricity consumption behavior analysis, load forecasting, tariff setting, demand-side response, etc
Combining the above figure and the date distribution corresponding to the clustering results of Gaussian mixture model (GMM) and K-Means ( Figure 5), it can be seen that the information obtained from the clustering results of K-Means is only that the electricity consumption of the building has small fluctuations in summer and low electricity consumption
A load classification method based on Gaussian mixture model clustering and principal component analysis is proposed
Summary
Load classification refers to the processing of load data from a large number of power devices to extract typical load profiles [1], which can be applied to electricity consumption behavior analysis, load forecasting, tariff setting, demand-side response, etc. The paper [15] compared several clustering algorithms and found that the divisional clustering algorithm was more efficient but less accurate Gaussian mixture model (GMM) clustering is used to assign cluster members according to the clustering probability, which is called "soft classification". It can effectively solve the problem of "hard assignment" with more information, and better clustering quality for largescale data sets. This paper proposes a hybrid PCA-GMM-based load state classification method by combining the advantages of "soft classification" and clustering flexibility of GMM clustering with PCA from the perspectives of improving clustering quality, clustering efficiency and saving computational cost. The results show that the proposed method has better clustering quality and clustering efficiency, and it does effectively reduce the computational cost
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