Multivariate Extreme Learning Machine Based AutoEncoder for Electricity Consumption Series Clustering

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.