In response to the low accuracy and high false alarm rate of anomaly identification in energy big data, this paper proposes a dynamic weighted identification method for energy big data anomalies based on optimal sub segment deep learning. This method deeply mines the optimal sub segments in energy big data, uses deep learning techniques to automatically learn advanced feature representations in the data, and combines dynamic weighting strategies to accurately identify abnormal behavior. By selecting the optimal sub segment, this method can focus on the data features most relevant to abnormal behavior, improving recognition accuracy. The experimental results show that the proposed method can capture subtle changes in abnormal energy big data, further enhancing the accuracy of recognition and reducing the false alarm rate. In summary, the method proposed in this article has high accuracy and low false alarm rate in the field of energy big data anomaly recognition, providing strong technical support for decision-making, optimization, and management in the energy industry.

Dynamic weighted identification method for energy big data anomalies based on optimal sub segment deep learning