AI-Driven Energy Auditing for Solar Heat Exchanger Optimization in Hybrid Geothermal-Solar Thermal Systems: A Review

Abstract

Hybrid geothermal-solar thermal systems have the potential to enhance renewable energy efficiency, but their performance may be hindered by variable solar input, temperature imbalances, and fouling. Maximizing efficiency requires optimizing critical components, particularly the solar heat exchanger, which ensures efficient thermal energy transfer between subsystems. Energy auditing plays a crucial role in identifying inefficiencies and optimizing solar heat exchanger performance by analyzing energy consumption patterns and providing valuable insights for efficiency improvements. This review explores energy auditing methodologies and the integration of artificial intelligence (AI)-driven optimization, with a focus on machine learning (ML) models for solar radiation and performance prediction, real-time fault detection, and adaptive system control. AI-driven approaches enhance efficiency and reduce operational costs by enabling data-driven adjustments in heat exchanger operation. However, challenges such as data quality issues, high computational demands, and model interpretability remain. Future research should focus on improving AI-based energy auditing techniques and addressing these challenges to further optimize system performance. Overall, AI-driven optimization integrated with energy auditing, presents a transformative approach to improving solar heat exchanger efficiency in hybrid renewable energy systems.