Electricity Anomaly Point Detection using Unsupervised Technique Based on Electricity Load Prediction Derived from Long Short-Term Memory

The primary focus of smart grid power analysis is on power load forecasting and data anomaly detection. Efficient and accurate power load prediction and data anomaly detection enable energy companies to develop reasonable production and scheduling plans and reduce waste. Since traditional anomaly detection algorithms are typically for symmetrically distributed time series data, the distribution of energy consumption data features uncertainty. To this end, a time series outlier detection approach based on prediction errors is proposed in this paper, which starts by using an attention mechanism-based convolutional neural network (CNN)-gated recursive unit (GRU) method to obtain the residual between the measured value and its predicted value, and the residual data generally conform to a symmetric distribution. Subsequently, for these residual data, a random forest classification algorithm based on grid search optimization is used to identify outliers in the power consumption data. The model proposed in this paper is applied to both classical and real energy consumption datasets, and the performance is evaluated using different metrics. As shown in the results, the average accuracy of the model is improved by 25.2% and the average precision is improved by 17.2%, with an average recall improvement of 16.4% and an average F1 score improvement of 26.8% compared to the mainstream algorithms.

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Electricity Anomaly Point Detection using Unsupervised Technique Based on Electricity Load Prediction Derived from Long Short-Term Memory

Cited by 3 publications

References 18 publications

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