Load curve clustering method based on improved spectral and multi-manifold clustering

Abstract

The invention discloses a load curve clustering method based on improved spectral and multi-manifold clustering. The load curve clustering method comprises three steps of typical daily load curve extraction, load curve clustering and clustering effect evaluation. Firstly, load characteristic indexes of a user are extracted, and typical daily load curves of the user are calculated and extracted bycombining a non-parameter kernel density estimation method; canonical warping distance metering curve similarity is introduced into an improved spectral and multi-manifold clustering algorithm, localsimilarity is calculated by adopting a Gaussian kernel function, and a similarity matrix is calculated based on the local similarity; and various clustering effectiveness indexes are adopted for evaluating a clustering result and algorithm performance after clustering. The local similarity adopts load data of a plurality of users in Baoding area as calculating example samples for performing clustering analysis, and verifies the rationality and superiority of a typical daily load curve extraction method and the improved spectral and multi-manifold clustering algorithm disclosed in the invention.

Description

technical field [0001] The invention relates to a load curve clustering method based on improved spectral multi-manifold clustering, which belongs to the field of photovoltaic forecasting. Background technique [0002] Clustering of power load curves is the basis for big data mining of power distribution and load management. Clustering user load curves through clustering technology in data mining to obtain reasonable user classifications will help electricity sales companies accurately grasp the characteristics of users' electricity consumption, introduce reasonable demand response mechanisms, and formulate scientific marketing strategies. It is of great significance to fill peaks and valleys, optimize power consumption curves, and improve power quality. [0003] The research on applying clustering technology to user load curve classification has been quite in-depth. Commonly used clustering algorithms mainly include: K-means, hierarchical clustering method, fuzzy C-mean (...

AI Technical Summary

Problems solved by technology

However, the dimension reduction process of the above methods will inevitably cause a certain degree of loss of original information and affect the quality of clustering.

In addition, many common dimensionality reduction methods such as principal component analysis (PCA) are based on the assumption that the data has a global linear distribution. If the data structure does not meet the requirements, it is difficult to achieve the ideal dimensionality reduction effect.

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