基于VMD和改进TCN的短期光伏发电功率预测

doi:10.16180/j.cnki.issn1007-7820.2023.03.007

Abstract

Abstract:

Photovoltaic power generation fluctuates, photovoltaic output is easily affected by various meteorological characteristics, and traditional TCN networks tend to over-enhance spatial characteristics and weaken individual characteristics. In view of these problem, a short-term photovoltaic power generation prediction model based on VMD and improved TCN is proposed in this study. The original photovoltaic power generation time series is decomposed into several modal components of different frequencies through VMD, and each modal component and the corresponding meteorological data are input to the improved TCN network for modeling and learning. The center frequency method is used to determine the optimal decomposition modal number of VMD. On the basis of the traditional TCN prediction model, DropBlock regularization is used to replace Dropout regularization to achieve the effect of suppressing information synergy in the convolutional layer, and the attention mechanism is introduced to autonomously mine and highlight the impact of key meteorological input characteristics and quantify the impact of various meteorological factors on photovoltaic power generation to improve forecasting precision. Based on the real data of a photovoltaic power station in Jiangsu, the simulation experiments show that the RMSE of the proposed prediction method is 0.62 MW and the MAPE is 2.03%.

Key words: photovoltaic power generation, variational modal decomposition, time-series convolutional neural network, DropBlock regularization, attention mechanism, power forecast, time series forecasting, data decomposition

CLC Number:

TP183

HUANG Yuan,WEI Yunbing,TONG Dongbing,WANG Weigao. Short-Term Photovoltaic Power Prediction Based on VMD and Improved TCN[J].Electronic Science and Technology, 2023, 36(3): 42-49.

Figures/Tables 12

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References 18

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k	u₁	u₂	u₃	u₄	u₅	u₆	u₇	u₈
3	0.001 4	0.138	0.272	-	-	-	-	-
4	0.001 3	0.135	0.236	0.331	-	-	-	-
5	0.000 2	0.025	0.145	0.253	0.398	-	-	-
6	0.000 2	0.024	0.143	0.239	0.320	0.405	-	-
7	0.000 1	0.019	0.123	0.189	0.254	0.326	0.476	-
8	0.000 1	0.020	0.096	0.152	0.240	0.317	0.395	0.477

预测模型	RMSE/MW	MAPE/%
LSTM	2.57	6.16
传统TCN	1.18	3.52
改进TCN	0.62	2.03

预测模型	RMSE/MW	MAPE/%
SVM	9.38	18.25
LSTM	4.29	10.32
VMD+LSTM	2.57	6.16
传统TCN	1.35	4.56
VMD+TCN	1.18	3.52
改进TCN	1.08	3.12
VMD+改进TCN	0.62	2.03

Short-Term Photovoltaic Power Prediction Based on VMD and Improved TCN

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