Neural network technology for managing big data in energy systems

Abstract

Neural network technology is becoming a core tool for managing big data in energy systems,significantly improving the accuracy and efficiency of load forecasting,state estimation,and fault detection.Compared to traditional methods,architectures such as Physics-Informed Neural Networks and LSTM perform better in terms of prediction error,robustness,and real-time capability.These technologies have reduced operational costs and delivered rapid returns in multiple real-world cases.In the future,federated learning,quantum computing,and edge intelligence will further drive energy systems toward adaptive,low-latency,and distributed intelligent operations.