Maximizing Battery Storage Efficiency and ROI in Grid-Connected Hybrid Solar Systems Using Real-Time Weather and Load Forecasting

Battery storage, Hybrid solar systems, Energy forecasting, Return on investment, Machine learning, Energy management systems, Grid integration.

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Vol. 13 No. 07 (2025)
Engineering and Computer Science
July 2, 2025

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This paper comprehensively analyzes maximizing battery storage efficiency and return on investment (ROI) in grid-connected hybrid solar systems. The proposed framework optimizes battery charging/discharging cycles by incorporating real-time weather and load forecasting while ensuring demand fulfillment and extending battery life. Experimental results show that integrating machine learning-based forecasting techniques can improve overall system efficiency by 17.3% and increase ROI by 22.5% compared to conventional systems. The study evaluates multiple energy management strategies across diverse geographical locations and load profiles, providing valuable insights for system designers and energy managers seeking to enhance the economic viability of renewable energy storage solutions.