HBRP Publication
Published March 31, 2026 | Version v1
Journal article Open

AI-Driven Smart Energy Management Platform for Renewable Energy Optimisation

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Description

The variability of solar and wind generation poses challenges for grid stability and efficiency. This paper presents a smart energy management system (SEMS) with an AI-driven software architecture to optimize renewable energy use in buildings and microgrids. The platform integrates IoT sensor data, machine learning forecasts (e.g. Support Vector Regression) for generation/load prediction, and a predictive scheduling algorithm for battery storage and load control. A microservices architecture with cloud/edge deployment is used for scalability and fault tolerance. In simulation with realistic profiles, the SEMS improves renewable self-consumption and reduces grid dependency, consistent with results in similar studies.

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References

  • 1. Z. Lyu et al., "Microservice-Based Architecture for an Energy Management System," IEEE Systems J., 2020. (Containerized microservices improved EMS scalability and reliability.)
  • 2. I. Núñez et al., "Design of a Microservices-Based Architecture for Residential Energy Efficiency Monitoring," Int. J. Electr. Telecommun., vol. 70, no. 4, 2024. (Proposes a cloud-IoT microservices EMS that significantly improves demand response and energy savings.)
  • 3. C. K. Rao et al., "IoT Enabled Intelligent Energy Management System with Advanced Forecasting and Load Optimization," Unconv. Res., vol. 4, 2024. (An IoT-based SEMS using SVM+PSO forecasting for planning; shows IoT-enabled forecasting improves day-ahead scheduling accuracy.)
  • 4. A. H. Bagdadee et al., "IoT-driven Hybrid Renewable Energy Integration for Enhanced Smart Home Efficiency," Sci. Rep., 15:41491 (2025). (IoT sensors and control in hybrid solar-wind-storage homes increased efficiency by ~72%, reduced costs and CO₂.)
  • 5. A. R. Singh et al., "ML-Based Energy Management and Power Forecasting in Grid-Connected Microgrids," Sci. Rep., 2025. (Employs SVR to forecast distributed generation; highlights that accurate forecasts are crucial for microgrid efficiency.)
  • 6. J. A. A. Silva et al., "IoT-Based EMS for AC Microgrids with Grid and Security Constraints," Appl. Energy, vol. 337, May 2023. (Presents a microservices IoT-EMS with a GUI, database, API, and MILP optimizer, validated on real microgrid data.)
  • 7. N. Reiners and L. Millet, "Application of the Open-Source Energy Management System OpenEMS in Hybrid Power Systems," 4th Hybrid Power Systems Workshop, 2019. (Uses OpenEMS platform to simulate PV-battery-diesel microgrid; shows open EMS can coordinate diverse sources and optimize generator dispatch for fuel savings.)
  • 8. L. L. Motta et al., "A Cloud Architecture for Home Energy Management Systems: A Conceptual Model," Energy Informatics, vol. 8:142 (2025). (Proposes a layered cloud–edge architecture for HEMS, highlighting scalable IoT data ingestion and analytics to support real-time monitoring and stakeholder needs.)
  • 9. C. K. Rao et al., "Review of IoT-Enabled Smart EMS for Photovoltaic Forecasting," Unconv. Res., vol. 9, 2026. (Reviews IoT-based PV energy management; emphasizes that reliable forecasting and cloud-based frameworks are required for effective smart grid operation.)
  • 10. F. Z. Elargoubi et al., "Advancements in Home Energy Management Systems: Review of Optimization Strategies," IFAC-PapersOnLine, vol. 58, no. 13, 2024. (Surveys HEMS techniques; finds that real-time control and renewable integration greatly reduce costs and emissions, and stresses IoT for user empowerment.)