Are hybrid approaches combining EKF-UKF and artificial neural networks key to unlocking the full potential of sustainable energy technologies and reducing environmental footprint?

Abstract

The integration of traditional state estimation techniques like the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) with modern artificial neural networks (ANNs) presents a promising avenue for advancing state estimation in sustainable energy systems. This study explores the potential of hybridizing EKF-UKF with ANNs to optimize renewable energy integration and mitigate environmental impact. Through comprehensive experimentation and analysis, significant improvements in state estimation accuracy and sustainability metrics are revealed. The results indicate a substantial 8.02 % reduction in estimation error compared to standalone EKF and UKF methods, highlighting the enhanced predictive capabilities of the hybrid approach. Moreover, the integration of ANNs facilitated a 12.52 % increase in renewable energy utilization efficiency, leading to a notable 5.14 % decrease in carbon emissions. These compelling outcomes underscore the critical role of hybrid approaches in maximizing the efficiency of sustainable energy technologies while simultaneously reducing environmental footprint. By harnessing the synergies between traditional filtering techniques and machine learning algorithms, hybrid EKF-UKF with ANNs emerges as a key enabler in accelerating the transition towards a more sustainable and resilient energy landscape.

Keywords

• Hybrid state estimation
• EKF
• UKF
• ANNs
• Renewable energy integration
• Carbon emissions reduction