With the opening of electricity market, the interaction between grids and users is becoming more and more frequent. Household electricity demand estimation is a significant and indispensable process of the necessary precise demand response in the future. Large‐scale coverage of the Advanced Metering Infrastructure provides a large volume of user electricity data and brings opportunities for residential electricity consumption forecasting, but, on the other hand, it has brought tremendous pressure on the communication link and data computing center. This paper proposes an efficient edge sparse coding method based on the K‐singular value decomposition (K‐SVD) algorithm to extract hidden usage behavior patterns (UBPs) from load datasets and reduce the cost of communication, storage, and computation. The load of representative household appliances is introduced as the initial dictionary of the K‐SVD algorithm in order to make the UBPs more proximate to the residents' daily electricity consumption. Then, a linear support vector machine (SVM)‐based method with UBPs is used to predict the subsequent interval household electricity demand. The experimental result shows that the proposed algorithm can effectively follow the trend of the real load curve and realize accurate forecasting of the peak electricity demand. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.