Day-Ahead Probabilistic Prediction Model for Photovoltaic Power Based on Combined Deep Learning

doi:10.11930/j.issn.1004-9649.202306080

Abstract

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.

Key words: probabilistic prediction, temporal convolutional network, long short-term memory network, attention mechanism

Yan GAO, Hanbin WU, Jixin ZHANG, Huaming ZHANG, Pei ZHANG. Day-Ahead Probabilistic Prediction Model for Photovoltaic Power Based on Combined Deep Learning[J]. Electric Power, 2024, 57(4): 100-110.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202306080

https://www.electricpower.com.cn/EN/Y2024/V57/I4/100

Figures/Tables 15

Fig.1 Results of correlation analysis of variables

Fig.2 Probabilistic prediction process of TCN-Attention-LSTM

Fig.3 Dilated causal convolution structure of TCN

Fig.4 Attention mechanism

Fig.5 Structure of TCN with attention mechanism

Fig.6 Structure of LSTM

Fig.7 Sliding window partitioning

Fig.8 Workflow of day-ahead probabilistic prediction of PV power

Table 1 Hyperparameters setting for main structure of TCN- Attention-LSTM

结构名称		超参数设置
TCN#1		i=2, o=10, p=3, d=1, k=4
TCN#2		i=10, o=10, p=9, d=3, k=4
TCN#3		i=10, o=10, p=18, d=6, k=4
LSTM		i_L=10, n_l=2, h_s=128
全连接层		i_N=128, o_N=58

Table 2 Common hyperparameters of model

参数		数值
学习率		0.001
批处理参数		32
优化器		Adam
训练集∶测试集		9∶1

Table 3 Evaluation metrics of each model within a 90% confidence interval and training time of different models

数据	模型	P_ICP/ %	P_INAW/ kW	S_W分数/ kW	训练时间/s
集中式	LSTM	93.79	0.2979	5.5948	1603.97
	TCN	96.57	0.3473	5.7086	121.89
	TCN-Attention	98.28	0.3693	5.9472	201.41
	TCN-LSTM	95.14	0.3259	5.7438	4260.87
	TCN-Attention-LSTM	97.18	0.3099	5.1763	5008.32
分布式	LSTM	88.68	0.3229	7.6214	912.94
	TCN	96.60	0.4497	10.2056	33.72
	TCN-Attention	92.08	0.3211	7.9540	122.67
	TCN-LSTM	94.34	0.3525	8.9185	1039.98
	TCN-Attention-LSTM	93.96	0.2674	7.2308	1168.41

Fig.9 Comparison of model predictions within a 90% confidence interval in sunny days

Fig.10 Comparison of model predictions within a 90% confidence interval in rainy days

Table 4 CRPS evaluation metrics for various models

数据	模型	C_RPS/kW
集中式	LSTM	0.0589
	TCN	0.0539
	TCN-Attention	0.0507
	TCN-LSTM	0.0433
	TCN-Attention -LSTM	0.0421
分布式	LSTM	0.0478
	TCN	0.0565
	TCN-Attention	0.0505
	TCN-LSTM	0.0445
	TCN-Attention -LSTM	0.0441

Fig.11 Probability density prediction results of TCN- Attention-LSTM

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