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Smart Management Consumption in Renewable Energy Fed Ecosystems.

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Summary

This study introduces a novel model for smart energy management systems, integrating embedded devices, AI, and IoT for efficient energy control in new and existing facilities. The proposed architecture enhances interoperability and reduces costs through edge and fog computing.

Keywords:
Internet of Thingsartificial intelligence paradigmscloud servicesembedded devicessmart grid

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Area of Science:

  • Energy Systems Engineering
  • Artificial Intelligence
  • Internet of Things (IoT)

Background:

  • Current energy management automation systems are limited by cloud service overload and lack of interoperability models for existing facilities.
  • Embedded electronic systems, new communication protocols, and AI offer potential for improved energy management.
  • Integrating diverse sensors, connectivity, and computing resources in embedded devices is crucial but faces implementation challenges.

Purpose of the Study:

  • To propose a model for integrating smart energy management systems in both new and existing facilities.
  • To leverage local embedded devices, IoT protocols, and AI-based services for enhanced energy control.
  • To utilize edge and fog computing for distributed service deployment within smart grid networks.

Main Methods:

  • Development of a distributed architecture supporting smart services and energy management control systems.
  • Integration of machine learning for consumption and generation prediction, electric load classification, and predictive maintenance.
  • Implementation using embedded devices, IoT communication protocols, and AI algorithms within a smart grid network.

Main Results:

  • A functional model for smart energy management was designed, developed, and tested in a facility with wind and solar generation.
  • The proposed system facilitates the development, cost reduction, and integration of new smart services.
  • Experimental testing confirmed the advantages of the model in self-consumption facilities.

Conclusions:

  • The proposed model enables seamless integration and interoperability of smart energy management systems in diverse facilities.
  • AI-driven smart grid facilities with IoT protocols and embedded devices reduce costs and facilitate new service deployment.
  • The developed method provides a pathway for designing, developing, and installing cost-effective smart services in self-consumption energy setups.