Deep Neural Networks in Power Systems: A Review

Abstract

Identifying statistical trends for a wide range of practical power system applications, including sustainable energy forecasting, demand response, energy decomposition, and state estimation, is regarded as a significant task given the rapid expansion of power system measurements in terms of scale and complexity. In the last decade, deep learning has arisen as a new kind of artificial intelligence technique that expresses power grid datasets via an extensive hypothesis space, resulting in an outstanding performance in comparison with the majority of recent algorithms. This paper investigates the theoretical benefits of deep data representation in the study of power networks. We examine deep learning techniques described and deployed in a variety of supervised, unsupervised, and reinforcement learning scenarios. We explore different scenarios in which discriminative deep frameworks, such as Stacked Autoencoder networks and Convolution Networks, and generative deep architectures, including Deep Belief Networks and Variational Autoencoders, solve problems. This study’s empirical and theoretical evaluation of deep learning encourages long-term studies on improving this modern category of methods to accomplish substantial advancements in the future of electrical systems.

Advanced Integration of Forecasting Models for Sustainable Load Prediction in Large-Scale Power Systems

“…Incorporating Variational Autoencoders (VAE) with Causal Convolutional Neural Networks (Causal CNN) enhances the model's ability to handle the complexity and uncertainty inherent in medium-to long-term power load forecasting [28]. The Causal CNN component focuses on the temporal relationships within the data, while the VAE aspect allows for the handling of uncertainties and variabilities in the power load data.…”