基于概率TCN-Transformer的短期光伏功率预测模型

doi:10.3969/j.issn.2097-0706.2024.11.002

摘要/Abstract

摘要：

提出了一种基于时序卷积网络（TCN）和Transformer结合的短期光伏发电功率预测方法。分析了风速、雨量、太阳辐照度、云量等影响光伏发电功率的主要因素；利用TCN提取序列的全局空间特征并采用Transformer提取序列中的长期依赖关系的时序特征，提出TCN-Transformer复合模型以实现高精度的光伏功率预测，并将其应用于光伏发电的确定性预测与概率预测中；通过澳大利亚沙漠知识太阳能中心（DKASC）数据集进行仿真分析。结果表明，改进后的TCN-Transformer模型在不同天气条件下均表现出优异的预测性能，有效提高了光伏功率的短期预测精度。

关键词: 光伏发电预测, 时序卷积网络, Transformer, 确定性预测, 概率预测

Abstract:

A short-term PV power prediction method based on a temporal convolutional network （TCN） and a Transformer structure is proposed. Firstly， the main factors affecting PV power generation，such as wind speed， rainfall， light intensity and cloudiness， are analysed. Then， TCN is used to extract the global spatial features of the sequence， and Transformer is used to extract the temporal features of long-term dependencies in the sequence， so that a TCN-Transformer composite model with a high prediction precision is applied to PV power deterministic and probabilistic prediction. Simulation analyses are performed on the dataset from DKASC（Australia）， and the results show that the improved TCN-Transformer model exhibits excellent prediction performance under different weather conditions， improving the short-term prediction accuracy on PV power.

Key words: photovoltaic power generation forecast, temporal convolutional network, Transformer, deterministic prediction, probabilistic prediction

中图分类号:

TM615

盛瑞祥, 张啸宇. 基于概率TCN-Transformer的短期光伏功率预测模型[J]. 综合智慧能源, 2024, 46(11): 10-18.

SHENG Ruixiang, ZHANG Xiaoyu. Photovoltaic power forecasting model based on probabilistic TCN-Transformer[J]. Integrated Intelligent Energy, 2024, 46(11): 10-18.

图/表 10

图1

图2

图3

图4

图5

表1

图6

表2

各模型评价指标

模型	MAE/kW	MSE/(kW)²	RMSE/kW	$R 2$	MAPE/%
CNN	0.818	3.432	1.853	0.943	56.72
TCN	1.093	3.792	1.868	0.899	32.66
LSTM	0.576	2.138	1.462	0.943	25.73
CNN-LSTM	0.547	2.132	1.460	0.941	17.85
Transformer	0.529	2.165	1.471	0.942	16.55
TCN-Transformer	0.447	1.814	1.347	0.969	15.02

表2

表3

不同天气评价指标

模型	天气	MAE/kW	MSE/(kW)²	RMSE/kW	$R 2$	MAPE/%
CNN	阴天	1.078	4.774	2.185	0.915	60.57
CNN	晴天	0.912	3.587	1.894	0.935	56.87
TCN	阴天	1.209	4.530	2.128	0.906	35.42
TCN	晴天	0.993	3.531	1.879	0.920	30.21
LSTM	阴天	0.759	2.554	1.598	0.917	27.47
LSTM	晴天	0.633	1.266	1.125	0.976	25.58
Transformer	阴天	0.668	2.452	1.566	0.921	23.48
Transformer	晴天	0.485	1.173	1.083	0.979	16.05
TCN-Transformer	阴天	0.732	1.889	1.374	0.931	19.98
TCN-Transformer	晴天	0.447	1.014	1.006	0.980	15.02

表3

图7

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参数	数值
输入通道数	96
输出通道数	36
隐藏层数	80
LSTM层数	1
注意力头数	3
失活率	0.3
批量大小	80
学习率	0.000 2