针对太阳辐射序列具有波动性以及受气象因素影响而导致太阳辐照度预测精度降低的问题,提出一种基于滑动窗口变分模态分解（SWVMD）、自适应图卷积网络（AGCN）和四核时间卷积神经网络（QTCN）的超短期太阳辐照度预测模型。首先利用SWVMD对历史辐射序列进行解耦,实时挖掘不同特征尺度的模态分量,然后将数据集重构为图数据,进而利用AGCN动态评估气象因素的影响程度,最后采用QTCN提取融合后特征序列的多尺度时序特征,实现对未来30 min太阳辐照度的预测。实验结果表明,与LSTM、TCN模型和CNN-Bi-LSTM模型相比,所提出的预测模型能有效提升预测精度。
Abstract
Because of the fluctuation of solar irradiation sequences and the influence of meteorological factors, the accuracy of solar irradiance prediction is reduced. An ultra-short-term solar irradiance prediction model based on sliding variational modal decomposition, adaptive graph convolution network and quad-kernel temporal convolutional network is proposed. Firstly, the historical irradiation series are decoupled by SWVMD to generate modal components with different feature scales in real time. Secondly, the original data set is reconstructed into graph data to dynamically evaluate the impact of meteorological factors through AGCN. Finally, the quad-kernel TCN model is constructed to extract the temporal features of the fused feature series, and predict the solar irradiance in the next 30 minutes. The experimental results show that compared with LSTM, TCN model and CNN-Bi-LSTM model, the proposed model can effectively improve the prediction accuracy.
关键词
太阳辐照度 /
深度学习 /
变分模态分解 /
图卷积神经网络 /
时间卷积神经网络
Key words
solar radiation /
deep learning /
variational mode decomposition /
graph convolutional networks /
temporal convolutional network
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] LIU J, HUANG X Q, LI Q, et al.Hourly stepwise forecasting for solar irradiance using integrated hybrid models CNN-LSTM-MLP combined with error correction and VMD[J]. Energy conversion and management, 2023, 280: 116804.
[2] JEON H J, CHOI M W, LEE O J.Day-ahead hourly solar irradiance forecasting based on multi-attributed spatio-temporal graph convolutional network[J]. Sensors, 2022, 22(19): 7179.
[3] AZIZI N, YAGHOUBIRAD M, FARAJOLLAHI M, et al.Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output[J]. Renewable energy, 2023, 206: 135-147.
[4] 孟安波, 许炫淙, 陈嘉铭, 等. 基于强化学习和组合式深度学习模型的超短期光伏功率预测[J]. 电网技术, 2021, 45(12): 4721-4728.
MENG A B, XU X C, CHEN J M, et al.Ultra short term photovoltaic power prediction based on reinforcement learning and combined deep learning model[J]. Power system technology, 2021, 45(12): 4721-4728.
[5] WU Z Q, WANG B.An ensemble neural network based on variational mode decomposition and an improved sparrow search algorithm for wind and solar power forecasting[J]. IEEE access, 2021, 9: 166709-166719.
[6] HUANG X Q, LIU J, XU S Z, et al.A 3D ConvLSTM-CNN network based on multi-channel color extraction for ultra-short-term solar irradiance forecasting[J]. Energy, 2023, 272: 127140.
[7] DHAKE H, KASHYAP Y, KOSMOPOULOS P.Algorithms for hyperparameter tuning of LSTMs for time series forecasting[J]. Remote sensing, 2023, 15(8): 2076.
[8] YANG D Z, KLEISSL J, GUEYMARD C A, et al.History and trends in solar irradiance and PV power forecasting: a preliminary assessment and review using text mining[J]. Solar energy, 2018, 168: 60-101.
[9] ZHOU Y, LIU Y F, WANG D J, et al.A review on global solar radiation prediction with machine learning models in a comprehensive perspective[J]. Energy conversion and management, 2021, 235: 113960.
[10] 倪超, 王聪, 朱婷婷, 等. 基于CNN-Bi-LSTM的太阳辐照度超短期预测[J]. 太阳能学报, 2022, 43(3): 197-202.
NI C, WANG C, ZHU T T, et al.Super-short-term forecast of solar irradiance based on CNN-Bi-LSTM[J]. Acta energiae solaris sinica, 2022, 43(3): 197-202.
[11] 谭海旺, 杨启亮, 邢建春, 等. 基于XGBoost-LSTM组合模型的光伏发电功率预测[J]. 太阳能学报, 2022, 43(8): 75-81.
TAN H W, YANG Q L, XING J C, et al.Photovoltaic power prediction based on combined XGBoost-LSTM model[J]. Acta energiae solaris sinica, 2022, 43(8): 75-81.
[12] ACIKGOZ H.A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting[J]. Applied energy, 2022, 305: 117912.
[13] 张雲钦, 程起泽, 蒋文杰, 等. 基于EMD-PCA-LSTM的光伏功率预测模型[J]. 太阳能学报, 2021, 42(9): 62-69.
ZHANG Y Q, CHENG Q Z, JIANG W J, et al.Photovoltaic power prediction model based on EMD-PCA-LSTM[J]. Acta energiae solaris sinica, 2021, 42(9): 62-69.
[14] 王福忠, 王帅峰, 张丽. 基于VMD-LSTM与误差补偿的光伏发电超短期功率预测[J]. 太阳能学报, 2022, 43(8): 96-103.
WANG F Z, WANG S F, ZHANG L.Ultra short term power prediction of photovoltaic power generation based on VMD-LSTM and error compensation[J]. Acta energiae solaris sinica, 2022, 43(8): 96-103.
[15] YU Y J, HU G P.Short-term solar irradiance prediction based on spatiotemporal graph convolutional recurrent neural network[J]. Journal of renewable and sustainable energy, 2022, 14(5): 053702.
[16] KHODAYAR M, WANG J H.Spatio-temporal graph deep neural network for short-term wind speed forecasting[J]. IEEE transactions on sustainable energy, 2019, 10(2): 670-681.
[17] 梁智, 孙国强, 李虎成, 等. 基于VMD与PSO优化深度信念网络的短期负荷预测[J]. 电网技术, 2018, 42(2): 598-606.
LIANG Z, SUN G Q, LI H C, et al.Short-term load forecasting based on VMD and PSO optimized deep belief network[J]. Power system technology, 2018, 42(2): 598-606.
[18] JIA M W, XU D Y, YANG T, et al.Graph convolutional network soft sensor for process quality prediction[J]. Journal of process control, 2023, 123: 12-25.
[19] 陈海鹏, 李赫, 阚天洋, 等. 考虑风电时序特性的深度小波-时序卷积网络超短期风功率预测[J]. 电网技术, 2023, 47(4): 1653-1662, 80-82.
CHEN H P, LI H, KAN T Y, et al. DWT-DTCNA ultra-short-term wind power prediction considering wind power timing characteristics[J]. Power system technology, 2023, 47(4): 1653-1662, 80-82.
[20] KUMARI P, TOSHNIWAL D.Deep learning models for solar irradiance forecasting: a comprehensive review[J]. Journal of cleaner production, 2021, 318: 128566.
[21] ZHAO Z N, YUN S N, JIA L Y, et al.Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features[J]. Engineering applications of artificial intelligence, 2023, 121: 105982.
PDF(2223 KB)
