基于组合相似日的变权多模型融合光伏预测方法

黄杨珏; 贾梦麒; 沈开程; 朱远哲; 汪进锋; 张晓珂

doi:10.14188/j.1671-8844.2023.0414

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基于组合相似日的变权多模型融合光伏预测方法

更新时间：2026-10-15

- 基于组合相似日的变权多模型融合光伏预测方法
- The variable-weight multi-model fusion PV forecasting method based on combined similar days
- 武汉大学学报（工学版） 2026年页码：1-8
- 作者机构：
  
  1.广东电网有限责任公司电力科学研究院，广东广州 510080
  2.广东省电力装备可靠性企业重点实验室，广东广州 510080
  3.武汉大学电气与自动化学院，湖北武汉 430072
- 作者简介：
  
  黄杨珏（1988-），女，高级工程师，主要从事配网设备研究，E-mail: hyjourney@126.com。
  贾梦麒（1997-），女，博士研究生，主要从事微电网控制研究，E-mail: mengqijia@whu.edu.cn。
- 基金信息：
  
  南方电网公司科技项目(GDKJXM20210194┣036100KK52210078)
- DOI：10.14188/j.1671-8844.2023.0414
  中图分类号： TM 615
- 收稿：2024-09-27，
  
  网络首发：2026-10-15，
- 稿件说明：
移动端阅览
黄杨珏, 贾梦麒, 沈开程, 等. 基于组合相似日的变权多模型融合光伏预测方法[J/OL]. 武汉大学学报（工学版）, 2026,1-8.

HUANG Yangjue, JIA Mengqi, SHEN Kaicheng, et al. The variable-weight multi-model fusion PV forecasting method based on combined similar days[J/OL]. Engineering Journal of Wuhan University, 2026, 1-8.
黄杨珏, 贾梦麒, 沈开程, 等. 基于组合相似日的变权多模型融合光伏预测方法[J/OL]. 武汉大学学报（工学版）, 2026,1-8. DOI： 10.14188/j.1671-8844.2023.0414.

HUANG Yangjue, JIA Mengqi, SHEN Kaicheng, et al. The variable-weight multi-model fusion PV forecasting method based on combined similar days[J/OL]. Engineering Journal of Wuhan University, 2026, 1-8. DOI： 10.14188/j.1671-8844.2023.0414.

摘要

针对光伏功率预测，首先改进传统的相似日理论，计算不同时刻对应的相似时刻数据构成组合相似日数据，以保障每一时段的相似性；然后构建分位数回归-长短期记忆网络预测模型，将相似日数据和多步功率数据分别作为输入，获取2组不同分位数下的光伏功率预测值；最后利用核密度估计，并根据相似日与待预测日的相似程度赋予2组输出不同的权重，得到最终的光伏预测功率值和概率预测区间。仿真案例表明，该预测方法的均方根误差为0.017 0，90%概率预测覆盖率为97.08%，平均带宽为0.087 8，与传统模型相比兼顾了预测的可靠性和敏锐性。

Abstract

For photovoltaic (PV) power prediction

firstly

the traditional similar day theory is improved by calculating the similar day data corresponding to different moments to constitute the combined similar day data to guarantee the similarity of each moment; secondly

a quantile regression-long short-term memory prediction model is constructed

and the similar day data and multi-step power data are used as inputs respectively to obtain the PV power prediction values under two different quartiles; finally

using kernel density estimation and assigning different weights to the two sets of outputs according to the degree of similarity between the similar day and the day to be predicted

the final PV predicted power values and probabilistic prediction intervals are obtained. The simulation case shows that the root mean square error of the proposed prediction method is 0.017 0

the 90% probability prediction coverage is 97.08%

and the average bandwidth is 0.087 8. Compared with the traditional model

the prediction reliability and sensitivity are taken into account.

关键词

Keywords

references

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