# Graph Convolutional Spectral Clustering for Electricity Market Data Clustering
## 1 Introduction  
With the deep integration of information and communication technology with power production and the comprehensive development of the smart grid and energy internet, there is an explosive growth trend in power data, accompanied by increasing diversity in data types and sources [1, 2]. Power data are transitioning from structured, small-scale, and low-speed data to the realm of big data in the electricity sector. Faced with massive electricity consumption information, challenges include long-term storage of data, complex data structures, high computational frequency, and disorderly mapping of logical relationships [3]. Addressing these characteristics requires expanding the storage capacity of electricity information collection systems, optimizing intelligent algorithms, and improving parallel computing efficiency in distribution grid automation systems. Therefore, there is an urgent need to employ novel data processing technologies and methods to extract the operational and societal service value embedded in the data [4]. Currently, research on power big data mainly focuses on its application status, key technologies, and prospects. [PERSON] [5], for instance, outlined the types and collection technologies of power big data in various operational scenarios of the power grid. [PERSON] et al. [6] analyzed the application prospects of power big data in enhancing lean management levels in the power grid, optimizing energy scheduling, transmission, and serving users. Additionally, some studies have explored the value inherent in power big data. For example, power big data plays a significant role and holds value in supporting decision making in energy enterprise management, developing energy-saving and environmentally friendly products, and providing comprehensive energy data services [7]. Power big data flows horizontally and vertically across different business areas in the power grid, contributingto precise grid governance and enhancing social service capabilities [8]. [PERSON] et al. [9] streamlined the data flow process to simplify intelligent distribution grid operations, proposing a data-driven value chain to improve the accuracy and adaptability of output results. [PERSON] et al. [10], based on information value theory, constructed a power service industry value chain from the supply side to the demand side and analyzed the business models of power big data. [PERSON] et al. [11] analyzed the current status of power big data applications and decision making, proposing critical paths and methods for power big data to support management decisions. In the realm of integrating and mining electricity consumption information, the current mainstream approach involves leveraging big data mining and clustering methods to unearth implicit associations among electricity consumption data. C3 Energy, a company based in the United States, has pioneered the development of the C3 Energy Analytics Engine platform in the field of integrating and analyzing large-scale electricity consumption data. Within this platform, two energy analysis tools have been developed to analyze the electricity consumption behavior of industrial users, commercial users, and residential users in distribution networks. [PERSON] et al. [12] explored the fusion of association rules and wavelet neural networks, constructing a real-time online cleansing model for big data. By conducting correlation analysis on data dimensions, the model differentiates between true data anomalies caused by equipment failures and anomalies resulting from sensor measurement errors. This is achieved through network parameter adjustments, enhancing the efficiency of data cleansing. [PERSON] et al. [13] focused on user time-series curves, employing symbol clustering for dimensionality reduction. Subsequently, Euclidean distance was used for similarity measurement on the reduced-dimensional data, obtaining reference curves for the original data. Interpolation fusion was then performed based on the reference curves to handle anomalies in the original curves.