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Machine learning

IoT-Driven Intelligent Energy Management: A Promising Solution for Sustainability

Strategic Insights

— 2 min read

Researchers from the Faculty of Engineering and Computing at Liwa University have developed a novel strategy that integrates Internet of Things (IoT) devices and Artificial Neural Networks (ANNs) to optimize energy usage and promote sustainability in residential settings. The study highlights the rapid expansion of IoT devices as a promising solution to the growing mismanagement of energy resources, which poses significant risks to both individuals and the environment. The integrated system enables continuous energy monitoring, real-time feedback, and scenario-based simulations, making it suitable for a wide range of home contexts.

Key Takeaways:

  • The study presents a novel, integrative strategy that combines IoT and ANNs to optimize energy usage and promote sustainability in residential settings.
  • The integrated system allows for continuous energy monitoring via modern IoT devices and wireless sensor networks, while ANNs-based prediction models evaluate consumption data to dynamically optimize energy use and reduce environmental impact.
  • The system's key features include simulated consumption scenarios and adaptive user profiles, which account for differences in household behaviors and occupancy patterns, allowing for tailored recommendations and energy control techniques.
  • The architecture of the system enables remote device control, real-time feedback, and scenario-based simulations, making it suitable for a wide range of home contexts.
  • The research concluded that the suggested system's feasibility and effectiveness are proved through detailed simulations, highlighting its potential to increase energy efficiency and encourage sustainable habits.
  • The study contributes to the rapidly evolving field of intelligent energy management by providing a scalable, integrated, and user-centric solution that bridges the gap between theoretical models and actual implementation.
  • The authors of the study include Azza Mohamed, Ibrahim Ismail, and Mohammed AlDaraawi from the Faculty of Engineering and Computing at Liwa University.

Statistics:

  • The study reports that the suggested system has the potential to increase energy efficiency by up to 30% and encourage sustainable habits.
  • The research used high-performance computing simulations to demonstrate the system's feasibility and effectiveness.
  • The integrated system has the potential to reduce environmental impact by up to 25%.
  • The study concludes that the suggested system is scalable and can be implemented in a wide range of residential settings.
  • The system uses a combination of IoT devices and ANNs-based prediction models to optimize energy use and reduce environmental impact.

Sources:

  • "IoT-Driven Intelligent Energy Management: Leveraging Smart Monitoring Applications and Artificial Neural Networks (ANN) for Sustainable Practices." Computers, 2025, 14(7):269. (Computers - http://www.mdpi.com/journal/computers)
  • DOI: https://doi.org/10.3390/computers14070269 (free version available at https://doi-org.sdpl.idm.oclc.org/10.3390/computers14070269)
  • NewsRx. Faculty of Engineering and Computing Researchers Update Understanding of Sustainability Research [IoT-Driven Intelligent Energy Management: Leveraging Smart Monitoring Applications and Artificial Neural Networks (ANN) for Sustainable Practices]. Ecology, Environment & Conservation. August 15, 2025; p 93.

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