Deep learning in power systems research: A review

With the rapid growth of power systems measurements in terms of size and complexity, discovering statistical patterns for a large variety of real-world applications such as renewable energy prediction, demand response, energy disaggregation, and state estimation is considered a crucial challenge. In recent years, deep learning has emerged as a novel class of machine learning algorithms that represents power systems data via a large hypothesis space that leads to the state-of-the-art performance compared to most recent data-driven algorithms. This study explores the theoretical advantages of deep representation learning in power systems research. We review deep learning methodologies presented and applied in a wide range of supervised, unsupervised, and semi-supervised applications as well as reinforcement learning tasks. We discuss various settings of problems solved by discriminative deep models including stacked autoencoders and convolutional neural networks as well as generative deep architectures such as deep belief networks and variational autoencoders. The theoretical and experimental analysis of deep neural networks in this study motivates long- term research on optimizing this cutting-edge class of models to achieve significant improvements in the future power systems research.