The transition to sustainable energy systems is needed to minimize the effects of climate change and decrease the use of fossil based energy, yet at the same time, there are some challenges associated with the introduction of renewable energy sources, such as intermittency, lack of demand and supply, storage capacity, and efficiency. The existing manuscript presents a comprehensive and methodical review of the smart green energy systems, specifically the use of Internet of Things and Artificial Intelligence technologies in the context of intelligent energy management. This paper is a systematized and in-depth review of AI and IoT enhanced green energy systems. The paper will first examine the concepts and characteristics of the green energy systems and examine critically the key challenges and smart energy management solutions. It then explains the IoT based architecture like smart metering, real-time monitoring and IoT enabled smart grids integration of renewable energy with some case studies in support of these. In addition, the paper provides a technical description of AI techniques, including Machine Learning, Deep Learning, and Reinforcement Learning, including the model design, input features, and performance indicators. Its applications in energy prediction, fault detection, predictive maintenance and anomaly detection are systematically examined. The results suggest that the combination of AI and IoT can significantly boost the accuracy of the forecasting process, improve the reliability of the system by identifying and addressing faults promptly, and allow to optimize distributed energy resources in real time with the help of the DERMS. The main issues surrounding data quality, scale, robustness, cybersecurity and trade-offs in sustainability are also addressed. The review provides a new cross-domain point of view and offers future research directions towards the creation of intelligent, resilient, and sustainable energy systems.
Shukla et al. (Mon,) studied this question.
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