Deep Learning Approaches for Power Load Forecasting

This article is a research paper that reviews deep learning-based methods for power load forecasting. It begins by discussing the significance of accurate power load forecasting for smart grid operations and highlights the advancements in deep learning that have made these methods increasingly popular in both academic and industrial settings. The paper introduces various deep learning models, such as convolutional neural networks, graph neural networks, recurrent neural networks, generative adversarial networks, and autoencoders, detailing their applications in power load forecasting. It also presents public datasets used for evaluating these models and discusses the results of benchmark experiments conducted on four representative forecasting models. Furthermore, the paper outlines current challenges in the field and summarizes future development trends for deep learning applications in power forecasting. The structured approach of the article allows researchers to understand the theoretical frameworks and methodologies involved, facilitating their application in other domains.