Data-Driven Approaches for Efficient Smart Grid Systems

This Research Topic aims to highlight the exciting potential of innovative forecasting methods and their practical applications using machine learning in smart grid systems (SGSs). Machine learning techniques, which encompass traditional neural networks and advanced deep learning methods, have gained significant attention for their ability to address the complex challenges within SGSs and simultaneously improve cost-effectiveness. It's important to note that when machine learning models are employed in SGSs, they primarily focus on forecasting. This emphasis is grounded in the models' impressive capability to accurately replicate the intricate dynamics that characterize smart grid systems. By harnessing these forecasting models, researchers and practitioners are equipped with a valuable tool to better understand and predict the behavior of SGSs. This not only contributes to academic advancements but also enhances the practical implementation of smart grid technologies.

Nonetheless, the diverse range of applications and inherent limitations of conventional machine learning techniques, particularly in the context of forecasting, present modeling challenges that demand the exploration of more efficient algorithms for optimal real-world performance. This Research Topic emphasizes the formulation of intricate forecasting problems using mathematical concepts and the creation of innovative machine learning algorithms, specifically tailored for smart grid systems (SGSs).

This Research Topic invites submissions that highlight impactful machine learning algorithms and pioneering forecasting applications for smart grid systems (SGSs). The topics of interest for this issue include, but are not limited to:
• Load forecasting
• Renewable energy prediction
• Energy consumption prediction
• Grid stability forecasting
• Predictive maintenance
• Probabilistic forecasting
• Distributed energy forecasting
• Energy market forecasting
• Energy storage prediction