Achieving environmental sustainability via an integrated shampoo optimized BiLSTM-Transformer model for enhanced time-series forecasting.
Journal:
Scientific reports
Published Date:
Jul 23, 2025
Abstract
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.
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