基于组合深度学习的光伏功率日前概率预测模型
Day-Ahead Probabilistic Prediction Model for Photovoltaic Power Based on Combined Deep Learning
高岩 1吴汉斌 1张纪欣 1张华铭 2张沛3
作者信息
- 1. 国网河北省电力有限公司保定供电分公司,河北保定 071000
- 2. 北京清软创新科技股份有限公司,北京 102208
- 3. 北京交通大学电气工程学院,北京 100089
- 折叠
摘要
为准确量化复杂场景下光伏预测功率的不确定性,提出了一种基于时序卷积网络-注意力机制-长短期记忆网络组合的光伏功率短期概率预测方法.首先,基于多种相关性分析方法选出与光伏功率强相关的气象因素;然后,基于时序卷积网络的特征提取能力和长短期记忆网络的时序特征建模能力,并结合注意力机制和分位数回归,建立组合深度学习预测模型;最后,采用核密度估计方法生成连续概率密度函数.以实际集中式和分布式光伏电站为案例进行分析,结果表明:与长短期记忆网络、时序卷积网络、时序卷积网络-注意力机制和时序卷积网络-长短期记忆网络相比,所提方法在确保最优预测区间的同时,可以提升概率密度预测的性能.
Abstract
To accurately quantify the uncertainty in the predicted photovoltaic(PV)power in complex scenarios,a short-term probabilistic prediction method for PV power based on a combination of temporal convolutional networks-attention mechanism-long short-term memory networks is proposed in this paper.Firstly,mete-orological factors strongly correlated with PV power are selected based on multiple correlation analysis methods.Then,a combined deep learning prediction model is built based on the feature extraction capability of the temporal convolutional network and the temporal feature modeling capability of the long and short-term memory network,combined with the attention mechanism and quantile regression.Finally,a kernel density estimation method is used to generate a continuous probability density function.The cases of actual centralized and distributed PV plants are analyzed,and the results show that compared with long short-term memory networks,temporal convolutional networks,temporal convolutional networks-attention mechanism,and temporal convolutional networks-long short-term memory networks,the proposed method can improve the performance of probability density prediction while ensuring the optimal prediction interval.
关键词
概率预测/时序卷积网络/长短期记忆网络/注意力机制Key words
probabilistic prediction/temporal convolutional network/long short-term memory network/attention mechanism引用本文复制引用
基金项目
国家电网河北省电力公司科技项目(kj2022-051)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据