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

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 a...