Optimization of Heat Transfer Processes in Energy Systems Using Artificial Intelligence

Abstract

The aim of this study is to develop a scientific and methodological framework for optimizing heat-transfer processes in energy systems using artificial intelligence and to describe the practical implementation of this framework through the AI Energy Heat Optimization digital platform. The research object is the heat-transfer process in energy systems, including heat loss, flow-rate regulation, insulation effect, solar heat collection, boiler efficiency, temperature control, and smart energy management. The article combines a PRISMA 2020-oriented literature search, a STROBE-informed observational evaluation design, a COREQ-based qualitative feedback logic, FAIR data principles, and current AI governance recommendations. Sources were selected from official and scientific databases, including the International Energy Agency, NIST, ISO, peer-reviewed journals on machine learning for heat transfer, HVAC predictive control, heat exchangers, thermal-energy storage, and physics-informed neural networks. The practical stage is represented by the AI Energy Heat Optimization platform, which contains a home page, a scientific concept block, a methodological framework, ten interactive simulations, a task bank, diagnostic self-assessment, expected-result indicators, and a certificate module. The platform is designed to demonstrate how artificial intelligence can assist in forecasting heat loss, adjusting thermal parameters, comparing traditional and AI-supported control, and forming evidence-based conclusions. The study shows that AI-based heat-transfer optimization should be evaluated not only by expected efficiency increase, but also by physical validity, data quality, interpretability, risk awareness, and practical usefulness. The proposed framework may be applied in applied scientific projects, STEM education, engineering-oriented research training, and early-stage digital prototyping of energy-efficiency solutions.