基于改进评价指标的波动性负荷短期区间预测

徐诗鸿; 张宏志; 林湘宁; 李正天; 卓毅鑫; 汪致洵; 随权

doi:10.7500/AEPS20190123002

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基于改进评价指标的波动性负荷短期区间预测

学术研究 | 更新时间：2025-06-16

- 基于改进评价指标的波动性负荷短期区间预测
- Improved Evaluation Index Based Short-term Interval Prediction of Fluctuation Load
- 电力系统自动化-方正平台 2020年44卷第2期页码：155-162
- 作者机构：
  
  1. 强电磁工程与新技术国家重点实验室，华中科技大学，湖北省武汉市 430074
  2. 华中科技大学电气与电子工程学院，湖北省武汉市 430074
  3. 广东电网有限责任公司管理科学研究院，广东省广州市 510080
  4. 广西电网公司电力调度控制中心，广西壮族自治区南宁市 530023
- 作者简介：
  
  徐诗鸿（1994—），男，通信作者，硕士研究生，主要研究方向：负荷预测、新能源并网及微网新技术。E-mail：375856335@qq.com
  张宏志（1993—），男，硕士，主要研究方向：电动汽车与新能源、微网及配电网新技术。
  林湘宁（1970—），男，教授，博士生导师，主要研究方向：电力系统保护与控制。
- 基金信息：
  
  国家自然科学基金重点资助项目(51537003);中国南方电网公司科技项目(GXKJXM20170244)
- DOI：10.7500/AEPS20190123002
  中图分类号：
- 收稿日期：2019-01-23，
  
  修回日期：2019-07-19，
  
  网络出版日期：2019-11-07，
  
  纸质出版日期：2020-01-25
- 稿件说明：
移动端阅览
徐诗鸿,张宏志,林湘宁等.基于改进评价指标的波动性负荷短期区间预测[J].电力系统自动化,2020,44(02):155-162.

XU Shihong,ZHANG Hongzhi,LIN Xiangning,et al.Improved Evaluation Index Based Short-term Interval Prediction of Fluctuation Load[J].Automation of Electric Power Systems,2020,44(02):155-162.
徐诗鸿,张宏志,林湘宁等.基于改进评价指标的波动性负荷短期区间预测[J].电力系统自动化,2020,44(02):155-162. DOI： 10.7500/AEPS20190123002.

XU Shihong,ZHANG Hongzhi,LIN Xiangning,et al.Improved Evaluation Index Based Short-term Interval Prediction of Fluctuation Load[J].Automation of Electric Power Systems,2020,44(02):155-162. DOI： 10.7500/AEPS20190123002.

摘要

针对传统点对点预测难以适用于波动性较大、不确定性较强的负荷的问题，提出了一种基于改进评价指标的区间预测方法，从区间宽度和累计误差2个角度对现有区间预测评价指标做出改进，提高了预测结果的合理性。在此基础上，从各个评价指标的自身特性及其对预测结果的影响程度进行考量，建立了区间预测综合评价指标，并利用神经网络构建了区间预测模型，以综合评价指标最优为目标，采用粒子群优化算法对网络结构参数进行训练优化，从而取得理想的波动性负荷区间预测效果。仿真中通过对某波动性较强的历史负荷数据进行预测分析，并与传统的点预测和区间预测方法进行对比，验证了所提方法的有效性和优越性。

Abstract

To solve the problem that traditional point-to-point prediction method is not applicable to the load with large fluctuation and uncertainty

this paper implements a prediction interval method based on improved evaluation index to improve existing forecasting evaluation index from two aspects of interval width and cumulative error

which enhances the reasonableness of prediction results. On this basis

weighing the characteristics and importance of each evaluation index for the influence on prediction results

the comprehensive evaluation index for interval prediction is established

and the interval prediction model is constructed by using neural network. Aiming at the optimization of the comprehensive evaluation index

the particle swarm optimization algorithm is used to train and optimize the structure parameters

so as to achieve ideal effect of interval prediction for fluctuating load. The historical load data with strong uncertainty is used to validate the proposed method. Comparing with the traditional point-to-point and interval forecasting methods

the results and analysis of the improved interval prediction verifies the effectiveness and superiority of the method.

关键词

Keywords

references

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