Methods of Analytical Model Optimization to Increase Energy Efficiency and Lower Emissions

Abstract

The strain on current power systems is increased by rising energy consumption. Fossil-fuel dependence continues producing huge carbon emissions. An improved hybrid model that combines solar, wind, and battery units is proposed in this study. Genetic Algorithm and Particle Swarm Optimization refine system behaviour in real time. Environmental inputs and load changes support effective operational predictions (Kim et al., 2023). Analytical approaches track monthly emissions, energy transfer, and storage losses. Results demonstrate reduced emissions and greater energy efficiency during twelve months. Emission estimates reached 0.12–0.14 kg CO₂ per kilowatt-hour. During favourable seasonal conditions, efficiency increased to about 86%. Dynamic control provided stable operation under changeable weather patterns. Reliable output during high demand periods was confirmed by primary data.
Comparative research showed notable improvements over previous static models (Ahmad et al., 2017). Adjustments made in real time improved battery scheduling and decreased reliance on the grid. The technique supports cleaner and adaptive hybrid systems for future use. It offers scalability across regions with diverse climate conditions. The concept promotes smart-grid readiness and supports long-term sustainability goals. Future research could include deeper AI predictions, blockchain verification, and resilience to extreme weather. This study develops practical modelling methodologies for next-generation renewable energy systems.