Smart Management Consumption in Renewable Energy Fed Ecosystems

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.