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

Djilali Khaine

doi:doi:10.11648/j.ajmie.20261105.14

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

Djilali Khaine^*

Published in American Journal of Mechanical and Industrial Engineering (Volume 11, Issue 5)

Received: 3 September 2026 Accepted: 20 September 2026 Published: 23 October 2026

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Abstract

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.

Published in	American Journal of Mechanical and Industrial Engineering (Volume 11, Issue 5)
DOI	10.11648/j.ajmie.20261105.14
Page(s)	132-140
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Artificial Intelligence, Internet of Things, Smart Buildings, Energy Efficiency, Advanced Materials, Phase Change Materials, Random Forest, LSTM, Sustainable Energy, Emerging Economies

References

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Khaine, D. (2026). AI-Driven Optimization of Smart Energy Systems Using Advanced Materials and IoT for Sustainable Environments in Emerging Economies. American Journal of Mechanical and Industrial Engineering, 11(5), 132-140. https://doi.org/10.11648/j.ajmie.20261105.14

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ACS Style

Khaine, D. AI-Driven Optimization of Smart Energy Systems Using Advanced Materials and IoT for Sustainable Environments in Emerging Economies. Am. J. Mech. Ind. Eng. 2026, 11(5), 132-140. doi: 10.11648/j.ajmie.20261105.14

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AMA Style

Khaine D. AI-Driven Optimization of Smart Energy Systems Using Advanced Materials and IoT for Sustainable Environments in Emerging Economies. Am J Mech Ind Eng. 2026;11(5):132-140. doi: 10.11648/j.ajmie.20261105.14

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@article{10.11648/j.ajmie.20261105.14,
  author = {Djilali Khaine},
  title = {AI-Driven Optimization of Smart Energy Systems Using Advanced Materials and IoT for Sustainable Environments in Emerging Economies},
  journal = {American Journal of Mechanical and Industrial Engineering},
  volume = {11},
  number = {5},
  pages = {132-140},
  doi = {10.11648/j.ajmie.20261105.14},
  url = {https://doi.org/10.11648/j.ajmie.20261105.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmie.20261105.14},
  abstract = {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.},
 year = {2026}
}

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TY  - JOUR
T1  - AI-Driven Optimization of Smart Energy Systems Using Advanced Materials and IoT for Sustainable Environments in Emerging Economies
AU  - Djilali Khaine
Y1  - 2026/10/23
PY  - 2026
N1  - https://doi.org/10.11648/j.ajmie.20261105.14
DO  - 10.11648/j.ajmie.20261105.14
T2  - American Journal of Mechanical and Industrial Engineering
JF  - American Journal of Mechanical and Industrial Engineering
JO  - American Journal of Mechanical and Industrial Engineering
SP  - 132
EP  - 140
PB  - Science Publishing Group
SN  - 2575-6060
UR  - https://doi.org/10.11648/j.ajmie.20261105.14
AB  - 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.
VL  - 11
IS  - 5
ER  -

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Djilali Khaine

Department of Mechanical Engineering, Faculty of Sciences, University of Tipaza, Tipaza, Algeria

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http://orcid.org/0000-0001-9409-0382

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Khaine, D. (2026). AI-Driven Optimization of Smart Energy Systems Using Advanced Materials and IoT for Sustainable Environments in Emerging Economies. American Journal of Mechanical and Industrial Engineering, 11(5), 132-140. https://doi.org/10.11648/j.ajmie.20261105.14

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ACS Style

Khaine, D. AI-Driven Optimization of Smart Energy Systems Using Advanced Materials and IoT for Sustainable Environments in Emerging Economies. Am. J. Mech. Ind. Eng. 2026, 11(5), 132-140. doi: 10.11648/j.ajmie.20261105.14

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AMA Style

Khaine D. AI-Driven Optimization of Smart Energy Systems Using Advanced Materials and IoT for Sustainable Environments in Emerging Economies. Am J Mech Ind Eng. 2026;11(5):132-140. doi: 10.11648/j.ajmie.20261105.14

Copy | Download

@article{10.11648/j.ajmie.20261105.14,
  author = {Djilali Khaine},
  title = {AI-Driven Optimization of Smart Energy Systems Using Advanced Materials and IoT for Sustainable Environments in Emerging Economies},
  journal = {American Journal of Mechanical and Industrial Engineering},
  volume = {11},
  number = {5},
  pages = {132-140},
  doi = {10.11648/j.ajmie.20261105.14},
  url = {https://doi.org/10.11648/j.ajmie.20261105.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmie.20261105.14},
  abstract = {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.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - AI-Driven Optimization of Smart Energy Systems Using Advanced Materials and IoT for Sustainable Environments in Emerging Economies
AU  - Djilali Khaine
Y1  - 2026/10/23
PY  - 2026
N1  - https://doi.org/10.11648/j.ajmie.20261105.14
DO  - 10.11648/j.ajmie.20261105.14
T2  - American Journal of Mechanical and Industrial Engineering
JF  - American Journal of Mechanical and Industrial Engineering
JO  - American Journal of Mechanical and Industrial Engineering
SP  - 132
EP  - 140
PB  - Science Publishing Group
SN  - 2575-6060
UR  - https://doi.org/10.11648/j.ajmie.20261105.14
AB  - 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.
VL  - 11
IS  - 5
ER  -

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