Intelligent analysis algorithm for power engineering data based on improved BiLSTM

• Published in: Scientific reports Vol. 15; no. 1; pp. 15320 - 16
• Main Authors: Xu, Yuanyuan, Yang, Jiapeng, Cai, Xin
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.05.2025; Nature Portfolio
• Subjects: 639/166; 639/301; 639/33; 639/4077; 639/624; 639/638; 639/705; 639/766; 639/925; Attention mechanism; Equipment fault diagnosis; Humanities and Social Sciences; Improved BiLSTM; Load forecasting; multidisciplinary; Power engineering data; Science; Science (multidisciplinary); Improved BiLSTM; Attention mechanism; Power engineering data; Load forecasting; Equipment fault diagnosis
• DOI: 10.1038/s41598-025-99409-7
• ISSN: 2045-2322

Traditional BiLSTM (Bidirectional Long Short-Term Memory) has limited processing capabilities for long-time series, insufficient adaptability to the characteristics of power engineering data, and inadequate accuracy in predicting problems in specific application scenarios. This paper introduces an attention mechanism to improve BiLSTM’s ability to capture long time series, integrate the time series of power engineering data, and design an intelligent analysis algorithm that adapts to multi-dimensional features. Experimental results show that in load forecasting, the mean square errors of the improved BiLSTM in summer and winter are 0.02 and 0.025 respectively, and R 2 are 0.985 and 0.982 respectively. In equipment fault diagnosis, the accuracy of improved BiLSTM under current, voltage, temperature and pressure is significantly higher than that of models such as GRU.(Gated Recurrent Unit) This paper improves BiLSTM, combines multi-dimensional feature fusion and multi-head self-attention mechanism, and optimizes it according to the characteristics of power engineering data, enhance noise robustness, and reduce noise impact by 8%. Although the training time and memory are slightly increased, the convergence speed is faster. The improved BiLSTM power engineering data analysis algorithm significantly improves the accuracy and robustness of tasks such as power load forecasting and equipment fault diagnosis by introducing self attention mechanism and multi-dimensional feature fusion. It is more adaptable in complex temporal patterns and multi-source data modeling.