Cloud-Based Smart Energy Management Systems for Renewable Energy
Authors
Department of Information Technology, Lincoln University College (Malaysia)
Department of Information Technology, Texas College of Management and IT (Nepal)
Article Information
DOI: 10.51244/IJRSI.2026.1306000080
Subject Category: Renewable energy
Volume/Issue: 13/6 | Page No: 1153-1168
Publication Timeline
Submitted: 2026-05-30
Accepted: 2026-06-04
Published: 2026-06-23
Abstract
The increasing global demand for clean and sustainable energy has accelerated the adoption of renewable energy sources such as solar and wind power. However, managing renewable energy efficiently remains a major challenge due to fluctuating environmental conditions, energy storage limitations, and the lack of real-time monitoring and intelligent control systems. Traditional energy management approaches often rely on manual supervision and isolated monitoring systems, which reduce operational efficiency and limit the ability to respond quickly to changes in energy production and consumption.
Cloud-Based Smart Energy Management Systems (SEMS) have emerged as a promising solution to address these challenges. By integrating cloud computing, Internet of Things (IoT) devices, and advanced data analytics, these systems enable real-time monitoring, intelligent decision-making, and remote management of renewable energy systems. IoT sensors continuously collect operational data such as voltage, current, temperature, and power output from solar panels and energy storage units. This data is transmitted to cloud platforms where it can be stored, analyzed, and used to optimize system performance.
Cloud-based architectures provide scalable data storage and powerful computing resources that allow large volumes of energy data to be processed efficiently. Through machine learning algorithms and predictive analytics, cloud platforms can identify performance trends, detect faults, and optimize energy distribution between loads, batteries, and the grid. These capabilities improve energy efficiency, reduce operational costs, and increase the reliability of renewable energy systems.
This paper explores the architecture, technologies, and benefits of cloud-based smart energy management systems in renewable energy environments. It also discusses practical applications in residential, industrial, and smart grid infrastructures. Additionally, the paper highlights key challenges such as data security, system reliability, and integration complexity.
Keywords
Cloud Computing, Renewable Energy, Smart Energy Management, Internet of Things (IoT), Real-Time Monitoring, Solar Energy, Smart Grid, Energy Optimization
Downloads
References
1. [1] I. E. Agency, Renewable Energy Market Update, Paris: IEA Publications, 2023. [Google Scholar] [Crossref]
2. [2] V. C. Gungor, D. Sahin, T. Kocak and e. al., "Smart grid technologies: Communication technologies and standards," IEEE Transactions on Industrial Informatics, vol. 7, no. 4, p. 529–539, 2013. [Google Scholar] [Crossref]
3. [3] X. Fang, S. Misra, G. Xue and D. Yang, "Smart grid—The new and improved power grid," IEEE Communications Surveys & Tutorials, vol. 14, no. 4, p. 944–980, 2012. [Google Scholar] [Crossref]
4. [4] Y. Zhang, J. Wang and H. Li, "Cloud-based monitoring and control system for photovoltaic power plants," Renewable Energy, vol. 168, p. 689–700, 2021. [Google Scholar] [Crossref]
5. [5] R. Gupta and P. Sharma, "IoT-based solar energy monitoring system for smart energy management," International Journal of Energy Research, vol. 44, no. 8, p. 6452–6465, 2020. [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- Work Function of Metal Back Contact Surface Alloy Molybdenum (Mo) With Tungsten (W) For Copper Indium Gallium Selenide (Cigs) Thin Film Solar Cell: Simulation Using Scaps-1d And Density Functional Theory (Dft) Using Winmostar Quantum Espresso
- Biodegradable Materials and Renewable Materials Innovation with Eggshell Powder in Sustainable Product Design
- Investigation of Zinc Cobaltite (ZnCo₂O₄) as a Hole Transport Layer for Perovskite Solar Cells: Implications for Renewable-Energy Research and Innovation
- Predictive Modeling of Demand Response Impact on Solar-Integrated Power Systems Using Bayesian Optimisation Long Short-Term Memory Neural Networks
- Gis-Based Spatial Classification of Onshore Wind Energy Potential in Nigeria