Scholarlink

<p>Accurate forecasting plays a vital role in enhancing the efficiency of power systems, ensuring better resource management, and supporting strategic decision-making. This work presents BiLSTM-Transformer, a hybrid deep learning model that integrates Bidirectional Long Short-Term Memory (BiLSTM) networks with Transformer architecture to improve predictive performance in complex time-series tasks. The model employs a second-order optimization approach using Shampoo, which strengthens convergence stability and promotes better generalization during training. By effectively modeling both short-term variations and long-range dependencies in meteorological data, BiLSTM-Transformer achieves superior forecast accuracy across multiple evaluation benchmarks. The results highlight its potential as a reliable tool for supporting sustainable energy planning and smart grid operations.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Achieving environmental sustainability via an integrated shampoo optimized BiLSTM-Transformer model for enhanced time-series forecasting

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月23日

查看全文
  • Asmaa Mohamed El-saieed,
  • Nada A. Dief

摘要

Accurate forecasting plays a vital role in enhancing the efficiency of power systems, ensuring better resource management, and supporting strategic decision-making. This work presents BiLSTM-Transformer, a hybrid deep learning model that integrates Bidirectional Long Short-Term Memory (BiLSTM) networks with Transformer architecture to improve predictive performance in complex time-series tasks. The model employs a second-order optimization approach using Shampoo, which strengthens convergence stability and promotes better generalization during training. By effectively modeling both short-term variations and long-range dependencies in meteorological data, BiLSTM-Transformer achieves superior forecast accuracy across multiple evaluation benchmarks. The results highlight its potential as a reliable tool for supporting sustainable energy planning and smart grid operations.