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As one of the key components of smart grid, advanced metering infrastructure (AMI) provides an immense number of data, making technologies such as data mining more suitable for electricity theft detection. However, due to the unbalanced dataset in the field of electricity theft, many AI-based methods such as deep learning are prone to under-fitting. To evade this problem and to detect as many types of theft attacks as possible, an outlier detection method based on clustering and local outlier factor (LOF) is proposed in this study. We firstly analyze the load profiles with k-means. Then, customers whose load profiles are far from their cluster centers are selected as outlier candidates. After that, the LOF is utilized to calculate the anomaly degrees of outlier candidates. Corresponding framework for practical application is then designed. Finally, numerical experiments based on realistic dataset show the good performance of the presented method. j c d Cut-off distance of cluster j. , LOF id Value of LOF for user i on day d. , rank id Rank of user i on day d based on descending order of LOFi,d ranki Average rank of user i during m-days. Functions  Size of a set. () f  Attack function. ( , ) dist  Euclidean distance between two data samples () n dist  The n-th nearest distance of a data sample.

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