Sparse-coding-based household clustering for demand response services

For the development of a smart grid, smart meter is a key device to measure the electric power usage of network-connected houses. Smart meters are currently being installed into households, and play the important role for providing a demand response service. A demand response is a necessary service in order to adjust supply-demand balancing because the balance is kept by the cost in the electricity market. Therefore, the customers of the electricity supply companies are expected to be optimally assigned as a group. In this paper, we separate a set of households into clusters as optimally assigned customers. When conducting the clustering, the utilization of unsupervised learning using data from a smart meter is required. In this study, we propose a method of household clustering for a demand response event by sparse coding, which is a type of neural network. The proposed method generates a power consumption model of each household, finds simple relationship distances between households, and conducts hierarchical clustering based on these distances. In addition, to extract the characteristics of fluctuating load usage, we conduct data normalization that cuts off at a fixed load usage of each household. To confirm the effect of the proposed clustering method, the usage tendency of household air conditioning (A/C) units was evaluated.