Abstract
Multivariate electricity consumption series clustering can reflect the trend of power consumption changes in the past time period, which can provide reliable guidance for electricity production. The dimensionality reduction-based method is an effective technology to address this problem, which obtains the low-dimensional features of each variate or all variates for multivariate time series clustering. However, most existing dimensionality reduction-based methods ignore the joint learning of the common representations and the variable-based representations. In this paper, we build a multivariate extreme learning machine based autoencoder model for electricity consumption clustering (MELM-EC), which performs common representation learning and variable-based representation learning simultaneously. MELM-EC maps the common representation and multiple variable-based representations to the original multivariate time series and computes the common output weights within a few iterations. Experimental results on realistic multivariate time series datasets and multivariate electricity consumption series datasets demonstrate the effectiveness of the proposed MELM-EC model.
Highlights
M ULTIVARIATE electricity consumption series clustering (MECSC) deals with multivariate series with the complex relationship, where each instance is composed of multivariate series which contain information related to each other [1]–[4]
We develop a novel multivariate extreme learning machine based autoencoder framework (MELM-EC) for the MECSC task, which is a joint learning framework for the common representations and the variable-based representations
We think that the common output weights of multivariate ELM (MELM)-EC can represent the features of the input multivariate electricity consumption series, and the common output weights can be obtained after a few iterations
Summary
M ULTIVARIATE electricity consumption series clustering (MECSC) deals with multivariate series with the complex relationship, where each instance is composed of multivariate series which contain information related to each other [1]–[4]. Some efforts demonstrated the effectiveness of dimensionality reduction methods (e.g., VPCA [15], CPCA [16], [17], and matrix factorization [18]) for MECSC and provided informative representations for clustering tasks. He et al [15] first used VPCA to construct an informative representation, and adopted the spatial weighted matrix distance to measure the similarity among all instances. We develop a novel multivariate extreme learning machine based autoencoder framework (MELM-EC) for the MECSC task, which is a joint learning framework for the common representations and the variable-based representations.
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