A Big Data Driven Quantum Energy Transformer Network for Renewable Energy Management in Smart Grids
Abstract
The fast integration of Renewable Energy Sources (RESs) into modern power systems introduces significant challenges in maintaining stability, efficiency, and reliable energy management in smart grids. The intermittent and uncertain nature of RESs like wind and solar energy requires high predictive models that can manage large-scale operational data. This work suggests a Quantum Energy Transformer Network (QETNet), a hybrid Deep Learning (DL) model designed for improving renewable energy prediction and management in smart grid (SG) environments using big data analytics. The proposed model combines classical transformer-based temporal feature extraction with a quantum-inspired feature learning model for capturing complex nonlinear relations with energy generation data. Experimental analysis is demonstrated on the Wind Turbine SCADA dataset and achieved MAE and $\mathrm{R}^{\mathrm{2}}$ values of 0.083 and 0.983. The outcomes show that the proposed quantum hybrid model captures the non-linear dynamics of generating renewable energy and improves prediction performance compared to other models. The proposed model offered a promising solution for better renewable energy management and decision-making in next-generation SG systems.
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