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Abnormal electricity consumption detection can detect abnormal electricity consumption behaviors in time and maintain a safe and stable power grid operating environment while reducing energy waste and economic losses. The popularity of smart meters makes it very easy to obtain electricity consumption data, which provides sufficient data support for data-driven abnormal electricity consumption detection methods. However, the problem of imbalanced data seriously affects the training effect of the model in practical application. Therefore, in this paper, a gated recurrent units based abnormal detection method for imbalanced electricity consumption data is proposed. The method adopts the borderline synthetic minority oversampling technique to realize the effective extension of the minority data. To better capture the time series characteristics of electricity consumption data, gated recurrent units are employed to classify electricity consumption data. To verify the effectiveness of this method, comparative experiments are done on imbalanced data. Experimental results show that this method has better data expansion effect and more accurate abnormal electricity consumption detection effect.