Application of Big Data and Machine Learning in Smart Grid

Dear Colleagues,

The development of smart grids is transforming energy systems by enabling intelligent, adaptive, and data-driven operations. With the integration of big data and machine learning (ML) technologies, smart grids can now manage vast volumes of heterogeneous data collected from sensors, smart meters, and distributed energy resources, and these technologies empower grid operators to optimize demand response, load balancing, power forecasting, efficiency benchmarking, and overall grid control with unprecedented accuracy and speed. Applications such as solar and wind power forecasting, anomaly detection, and predictive maintenance are reshaping how renewable energy is integrated and managed across the grid. Despite these advancements, several challenges remain, including data privacy, model interpretability, scalability, and the integration of real-time analytics into legacy infrastructure.

This Special Issue aims to gather novel research, innovative methodologies, and applied case studies exploring the use of big data and ML in smart grid systems. We welcome contributions addressing theoretical developments, algorithm design, and real-world implementations—especially those that leverage interdisciplinary approaches or focus on practical solutions for sustainable and resilient energy systems. Areas of interest for this Special Issue include, but are not limited to, the following topics:

• Big data architecture and platforms for smart grid applications;
• Machine learning algorithms for load and energy demand forecasting;
• Solar and wind power prediction using AI and data-driven methods;
• Demand response optimization using real-time data analytics;
• Deep learning approaches for anomaly detection and fault diagnosis in power systems;
• Integration of distributed renewable energy sources using predictive analytics;
• Efficiency benchmarking and performance monitoring in smart grid operations;
• Reinforcement learning for smart grid control and decision-making;
• Federated learning and privacy-preserving data analytics in energy systems;
• Hybrid models combining physics-based and machine learning approaches;
• Data fusion and feature engineering for improving forecasting accuracy;
• Edge and fog computing for decentralized smart grid intelligence;
• Case studies on AI-enabled energy management in microgrids and virtual power plants;
• Visualization and explainability of machine learning models in grid operations;
• Challenges and opportunities in real-time ML deployment in critical infrastructure.

Dr. Haosen Yang
Dr. Ziyu Chen
Dr. Xiaoming Liu
Dr. Qin Wang
Guest Editors

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