Author

V. Bereznychenko

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Open access Jul 2026

Hybrid temporal deep learning and ensemble regression framework for remaining useful life prediction of lithium-ion batteries in energy storage systems

A hybrid data-driven framework integrating a Temporal Convolutional Network, Bidirectional Long Short-Term Memory, and Extreme Gradient Boosting for accurate LIB RUL prediction provides a robust and computationally efficient solution for intelligent battery health monitoring, predictive maintenance, and smart battery management applications in electric vehicles and energy storage systems.

T. Mariprasath, Kumaresh S. S., Seif Al Bustanji et al. · Energy Exploration & Exp... · 0 citations

Open access Aug 2026

A physics-constrained temporal modeling framework for robust short-term wind power forecasting

The integration of physics-guided constraints with the temporal convolutional architecture significantly enhances prediction accuracy, stability, and generalization capability, making it suitable for real-time wind energy forecasting applications, intelligent energy management systems, and microgrid power system operations.

S. Marisargunam, T. Mariprasath, Mohit Bajaj et al. · Energy Exploration & Exp... · 0 citations