Review on Smart Meter Data Clustering and Demand Response Analytics

The development of smart grids and worldwide spread of smart meters enable a great opportunity to develop the demand-side management. How to apply the analysis of household smart meter data to improve energy efficiency and implement demand response schemes has become a new hot field. This paper presents an overview of the literature on the most recent methods of smart meter data clustering analysis. Mainly includes the latest applications of hierarchical, k-means clustering, self-organizing maps, principal and components analysis and other algorithms in smart meter data analysis. Furthermore, this paper reviews the application of smart meter data analysis in demand response potential estimation and candidate selection, the action analysis and scheme management of demand response. Through the literature review, we find the crucial points in the existing literature. Due to the characteristics of data, data feature extraction and form conversion are required before clustering. To ensure the effectiveness of demand response schemes, power companies need to overcome the random characteristics of users and conduct various feasibility studies around customers. Through a comprehensive review in this paper, we believe that with the solution of these crucial points, the application of smart meter data will have broad prospects.

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