计算机与现代化 ›› 2024, Vol. 0 ›› Issue (12): 108-115.doi: 10.3969/j.issn.1006-2475.2024.12.016

• 信息系统 • 上一篇    下一篇

  超短时电力负荷预测技术研究进展



  

  1. (1.国网浙江省电力有限公司宁波供电公司,浙江 宁波 315000; 
    2.宁波市电力设计院有限公司,浙江 宁波 315000; 3.上海电力大学计算机科学与技术学院,上海 201306)
  • 出版日期:2024-12-31 发布日期:2024-12-31
  • 基金资助:
    国家自然科学基金通用技术联合基金重点项目(U1936213)

Research Progress in Ultra Short-term Power Load Forecasting Technology 

  1. (1. Ningbo Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Ningbo, 315000, China;
    2. Ningbo Electric Power Design Institute Co.,Ltd., Ningbo 315000, China;
    3. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 201306, China)
  • Online:2024-12-31 Published:2024-12-31

摘要: 超短期预测在许多领域有广泛的应用场景。超短期电力负荷预测对于电力的实时调度、资源分配具有重要意义。合理的电力调度能够提升居民的用电体验,同时避免资源浪费。随着我国用电结构越来越多样化,对电力负荷进行准确的预测逐渐成为了难题。本文介绍超短期电力负荷预测的应用场景以及当前面对的困难与挑战,从技术层面将目前主流超短期电力负荷预测使用的方法分为传统预测方法、智能预测方法和组合预测方法,并对每一类方法内的模型按照实现方式进行细分和归类,随后在介绍过程中对部分典型模型的原理进行解释,最后,对比总结3类方法的优缺点,以表格的形式直观展示文中提到的部分模型的特点,并对未来超短期电力负荷预测的研究方向提出合理的建议。

关键词: 超短期预测, 电力负荷预测, 传统预测, 组合预测

Abstract: Ultra-short term forecasting has wide applications in various fields. Accurate prediction of power load in the ultra-short term is of great significance for real-time scheduling and resource allocation in the power sector. Effective power scheduling enhances the consumer experience while avoiding resource wastage. With the increasing diversification of power consumption structure in our country, accurately predicting power load has become a challenging task. This article introduces the application scenarios of ultra-short term power load forecasting, as well as the current difficulties and challenges faced. From a technical perspective, the mainstream methods for ultra-short term power load forecasting are categorized into traditional forecasting methods, intelligent forecasting methods, and combination forecasting methods. Each category is further divided and classified based on implementation approaches. Furthermore, the principles of some representative models within each category are explained. Finally, the advantages and limitations of these three types of methods are compared and summarized. A table is provided to intuitively demonstrate the characteristics of some mentioned models. Reasonable suggestions for future research directions in ultra-short term power load forecasting are also proposed.

Key words:  , ultra-short term forecasting; electric load forecasting; traditional forecasting; ensemble forecasting

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