DOI: 10.11648/j.ajmie.20261105.14 ISSN: 2575-6060

AI-Driven Optimization of Smart Energy Systems Using Advanced Materials and IoT for Sustainable Environments in Emerging Economies

Djilali Khaine
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The transition toward sustainable energy systems requires the integration of advanced digital technologies, intelligent control strategies, and high-performance materials capable of improving energy efficiency while reducing environmental impacts. In this context, the convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and advanced energy materials offers significant opportunities for developing more adaptive, predictive, and sustainable energy systems. This study proposes an integrated framework combining AI, IoT, and advanced energy materials for the monitoring, prediction, optimization, and intelligent management of smart energy systems. The proposed architecture is structured into four complementary layers: intelligent sensing and IoT-based data acquisition, secure data transmission and data preprocessing, AI-based prediction and decision-making, and dynamic energy optimization. Linear Regression (LR), Random Forest (RF), and Long Short-Term Memory (LSTM) models are considered as representative approaches for energy-consumption prediction, enabling the analysis of relationships between energy demand and relevant environmental and operational variables. The methodology incorporates climatic conditions, indoor temperature and humidity, occupancy, energy demand, solar-related parameters, and material-related thermal characteristics. Advanced materials, including thermal energy storage and phase-change materials, are considered as functional components capable of improving thermal stability and reducing energy demand. Experimental and real-world evidence from international building-energy studies supports the feasibility of the IoT-AI and advanced-material components. In addition, the UCI Appliances Energy Prediction dataset provides an experimentally measured benchmark for investigating data-driven energy prediction using real building-energy and environmental measurements. The integrated framework further introduces multi-objective optimization to balance energy consumption, operating costs, carbon emissions, thermal performance, and system constraints. By establishing a continuous feedback loop between sensing, prediction, optimization, and control, the proposed approach provides a flexible basis for intelligent energy management. The framework is designed to be transferable to buildings and distributed energy systems in emerging economies, where energy efficiency, environmental sustainability, digitalization, and resource constraints represent major challenges. Overall, the study highlights the potential of integrating AI, IoT, and advanced materials as complementary technologies for developing resilient, efficient, and sustainable smart energy systems.

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