Review and Prospect of Load Forecasting Based on Deep Learning

被引：1
作者：
Ma, Hengrui [1 ]
Yuan, Aotian [1 ]
Wang, Bo [2 ]
Yang, Changhua [3 ]
Dong, Xuzhu [2 ]
Chen, Laijun [1 ]
机构：
[1] School of Energy and Electrical Engineering, Qinghai University, Xining
[2] School of Electrical Engineering and Automation, Wuhan University, Wuhan
[3] School of Electrical Engineering and New Energy, China Three Gorges University, Yichang
来源：
Gaodianya Jishu/High Voltage Engineering | 2025年 / 51卷 / 03期
基金：
中国国家自然科学基金;
关键词：
artificial intelligence technology; deep learning; double carbon; load forecasting; neural network; new power system;
D O I：
10.13336/j.1003-6520.hve.20241558
中图分类号：
学科分类号：
摘要：
Constructing a new type of power system is an important means to promote the transformation and development of modern power systems and achieve the dual-carbon goal. Accurate load forecasting results are crucial for optimizing the balance of power supply and demand and enhancing energy utilization efficiency, and artificial intelligence (AI) technology represented by deep learning can effectively optimize the balance of power supply and demand and enhance energy utilization efficiency. AI technology represented by deep learning can effectively optimize the balance of power supply and demand and improve energy utilization efficiency. Based on this, the paper firstly analyzes the current status of load forecasting research from the perspectives of scene objects, data types, evaluation methods, forecasting methods, etc., and systematically evaluates and summarizes the development history, advantages and disadvantages of the existing deep learning-based load forecasting methods for power systems. Finally, in view of the challenges of load forecasting under the new type of power system, the research outlook of the future technology is made from the model and scenario levels, respectively. © 2025 Science Press. All rights reserved.
引用
收藏
页码：1233 / 1250
页数：17
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