Considering that there exists a high cost and a low accuracy of low-voltage transformer area topology using artificial verification, this paper proposes a new automatic verification method based on sparse adaptive learning. Based on the massive user electricity consumption data, a parametric user electricity consumption model of low-voltage transformer areas was constructed. Then, a sparse adaptive learning algorithm was proposed. By utilizing a threshold testing, users who do not belong to the transformer area were identified. The performance of the proposed method was tested using the electricity consumption data of a certain transformer area in Haining, Zhejiang province. Experimental results showed that the proposed method can achieve a good estimation performance. In the simulative cases, the proposed method can achieve 100% accuracy ratio and 100% recall ratio. In the real cases, it can achieve 84.5% accuracy ratio and 90.7% recall ratio.
