Abstract: The energy consumption of the office equipment can be described by linear regression model. For the model, a multiple outlier detection algorithm based on single-link hierarchical clustering and LTS (least trimmed squares) estimator is proposed. This method is validated in different types of typical data sets. And the results prove that it has excellent performance. Then it is applied to office equipment energy consumption data sets. The experiments show that it has better ability to deal with the masking and swamping problems than other algorithms. Also, the method can not only correctly identify the outliers but also provide abnormal degree of outliers. The managers can develop the reasonable energy management solutions and achieve the purpose of energy saving.