Short-term Interval Probability Prediction of Photovoltaic Power Based on Weather Similarity Clustering and Quantile Regression Neural Network

ZHAO Yao; GAO Shaowei; LI Dongdong; LIN Shunfu; YANG Fan; HUANG Xueqin

doi:10.7500/AEPS20230619002

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Short-term Interval Probability Prediction of Photovoltaic Power Based on Weather Similarity Clustering and Quantile Regression Neural Network

Basic Research | 更新时间：2025-06-16

- Short-term Interval Probability Prediction of Photovoltaic Power Based on Weather Similarity Clustering and Quantile Regression Neural Network
- Automation of Electric Power Systems Vol. 47, Issue 23, Pages: 152-161(2023)
- 作者机构：
  
  上海电力大学电气工程学院，上海市 200090
- 作者简介：
- 基金信息：
  
  Shanghai Rising-Star Program(21QC1400200);Natural Science Foundation of Shanghai(21ZR1425400);National Natural Science Foundation of China(52377111)
- DOI：10.7500/AEPS20230619002
  CLC：
- Received：19 June 2023，
  
  Revised：2023-09-18，
  
  Published Online：23 October 2023，
  
  Published：10 December 2023
- 稿件说明：
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赵耀,高少炜,李东东等.基于天气相似聚类与QRNN的短期光伏功率区间概率预测[J].电力系统自动化,2023,47(23):152-161.

ZHAO Yao,GAO Shaowei,LI Dongdong,et al.Short-term Interval Probability Prediction of Photovoltaic Power Based on Weather Similarity Clustering and Quantile Regression Neural Network[J].Automation of Electric Power Systems,2023,47(23):152-161.
赵耀,高少炜,李东东等.基于天气相似聚类与QRNN的短期光伏功率区间概率预测[J].电力系统自动化,2023,47(23):152-161. DOI： 10.7500/AEPS20230619002.

ZHAO Yao,GAO Shaowei,LI Dongdong,et al.Short-term Interval Probability Prediction of Photovoltaic Power Based on Weather Similarity Clustering and Quantile Regression Neural Network[J].Automation of Electric Power Systems,2023,47(23):152-161. DOI： 10.7500/AEPS20230619002.

摘要

为减少光伏发电的不确定性对电力系统造成的影响，描绘更为准确、清晰的光伏出力区间，提出一种基于天气相似聚类与分位数回归神经网络(QRNN)单调模型的短期光伏功率区间概率预测模型。首先，QRNN单调模型在预测过程中引入单调性，保证单调的分位数结果，并采用Huber范数近似替代原损失函数，弥补了传统区间预测分位数交叉及损失函数不可微的缺陷。其次，基于气象信息的数据特征，采用动态自组织映射聚类算法，通过神经元竞争与更新确定神经元邻域权重结构，并根据其邻域权重大小将天气聚类为晴天、多云天与阴天，得到相似天气下的数据集。然后，在不同天气条件下对影响光伏出力的因素进行相关性分析，得到不同天气下的天气影响特征，并作为输入因子输入神经网络中。最后，以澳大利亚沙漠知识太阳能中心实际数据集为例进行区间预测，并采用核密度估计给出概率预测结果，验证了所提方法的可靠性。

Abstract

In order to reduce the influence of the uncertainty of photovoltaic power generation on the power system and describe the photovoltaic power output interval more accurately and clearly

a short-term interval probability prediction model of photovoltaic power based on the weather similarity clustering and quantile regression neural network (QRNN) monotone model is proposed. Firstly

the monotonicity of QRNN monotone model is introduced in the prediction process to ensure the monotonicity of quantile results

and the Huber norm is used to replace the original loss function

which makes up the defects of the traditional interval prediction of quantile crossing and the non-differentiable loss function. Secondly

based on the data characteristics of meteorological information

the dynamic self-organizing mapping clustering algorithm is used to determine the neuronal neighborhood weight structure through neuronal competition and updating. According to the neighborhood weight

the weather is classified into sunny

cloudy and overcast days

and the data sets under similar weather are obtained. Then

the correlation analysis of the factors affecting the photovoltaic output under different weather conditions is carried out

and the weather influence characteristics under different weather are obtained and input into the neural network as input factors. Finally

the actual data set of Australian Desert Knowledge Solar Energy Center is taken as a case to make interval prediction

and the probability prediction results are given by kernel density estimation

which verifies the reliability of the proposed method.

关键词

Keywords

references

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