基于大数据技术的电力系统负荷预测模型研究  

Research on Load Forecasting Model of Power System Based on Big Data Technology

作　　者：张鹏飞 ZHANG Pengfei

机构地区：[1]国网新能源云技术有限公司,北京100077

出　　处：《电力系统装备》2024年第11期99-100,119,共3页Electric Power System Equipment

摘　　要：随着大数据技术的发展,电力系统负荷预测迎来了新的机遇和挑战。文章针对当前电力系统负荷预测中存在的问题,提出了一种基于大数据技术的电力系统负荷预测模型。该模型通过整合多源异构数据,运用机器学习算法和深度学习方法,实现了高精度、实时性和鲁棒性的负荷预测。试验结果表明,该模型相比传统方法具有显著优势,为电力系统的优化调度和运行控制提供了有力支持。With the development of big data technology,power system load forecasting has ushered in new opportunities and challenges.The article proposes a power system load forecasting model based on big data technology to address the problems existing in current power system load forecasting.This model integrates heterogeneous data from multiple sources and utilizes machine learning algorithms and deep learning methods to achieve high-precision,real-time,and robust load forecasting.The experimental results show that the model has significant advantages compared to traditional methods,providing strong support for the optimization scheduling and operation control of power systems.

关 键 词：大数据 电力系统 负荷预测 机器学习 深度学习 

分 类 号：TM715[电气工程—电力系统及自动化]