Hybrid cloud systems are increasingly used to provide scalable, flexible, and cost-effective computing services. However, managing resources efficiently in these environments remains a major challenge due to changing workload demands and security requirements. This paper proposes an electronics-aware resource provisioning framework for secure and scalable hybrid cloud systems. The framework integrates Artificial Intelligence (AI), Machine Learning (ML), IoT-enabled edge computing, FPGA/TPU accelerators, Kubernetes auto-scaling, and Zero-Trust Security to optimize resource allocation across private and public cloud environments. The proposed approach aims to minimize provisioning cost, energy consumption, response time, and Service Level Agreement (SLA) violations while improving resource utilization and Quality of Service. The findings suggest that combining cloud technologies with modern electronics-based resources enhances scalability, operational efficiency, and security, making hybrid cloud infrastructures more sustainable and future-ready..

The increasing adoption of photovoltaic (PV) systems has created a need for efficient control strategies to maximize energy extraction under dynamic environmental conditions. This study presents the development of a novel Maximum Power Point Tracking (MPPT) scheme for photovoltaic systems subjected to variations in solar irradiance, temperature, cloud movement, and partial shading conditions. The proposed MPPT controller was designed and evaluated using MATLAB/Simulink by integrating a PV model, DC–DC boost converter, and advanced control mechanism. The performance of the proposed method was compared with the conventional Incremental Conductance (INC) technique. Simulation results demonstrated that the proposed MPPT scheme achieved a maximum output power of 297.6 W with a tracking efficiency of 98.4%, faster settling time of 0.08 seconds, and reduced power ripple. The controller exhibited improved adaptability, voltage regulation, and converter stability under rapidly changing weather conditions. The developed approach provides an effective solution for enhancing PV system reliability and improving solar energy utilization in grid-connected, standalone, and renewable energy applications..

The present study focuses on the design and performance optimization of a Solar PV–Battery Hybrid Electric Vehicle (EV) Charging System using MATLAB/Simulink. The proposed system integrates a 5-kW photovoltaic array, Maximum Power Point Tracking (MPPT) controller, battery energy storage system, and hybrid energy management strategy to improve renewable energy utilization and charging reliability. The model was developed and evaluated under varying solar irradiance, temperature, EV charging demand, and battery operating conditions for Delhi NCR. Simulation results demonstrated effective power management, stable battery State of Charge (SOC), and enhanced charging performance. The optimized system achieved an average PV output of 2582 W, MPPT output of 2484 W, EV charging power of 3638 W, system efficiency of 97.9%, and renewable energy contribution of 66.8%. The developed framework provides an efficient and sustainable solution for renewable energy-based EV charging infrastructure..

The present study developed a Machine Learning-Based Energy Load Prediction and Management framework for Hybrid Renewable Energy Systems (HRES) to address the challenges of renewable energy uncertainty, dynamic load variations, and efficient power utilization. The proposed approach integrated renewable energy sources, energy storage systems, and intelligent machine learning-based forecasting techniques to improve system reliability and sustainability. Historical energy consumption data, renewable generation parameters, and environmental factors such as solar irradiance, wind speed, temperature, humidity, battery state of charge, and previous load demand were used for developing predictive models. Data preprocessing techniques, including normalization, noise reduction, missing value handling, and feature scaling, were applied to enhance data quality and improve model performance. .