为提升短期太阳辐射预测的准确性,提出一种基于ICEEMDAN-LSTM和残差注意力的短期太阳辐照度预测方法。该方法利用改进的自适应噪声完备集合经验模态分解（ICEEMDAN）将原始辐射序列分解为多尺度模态分量,同时引入残差注意力机制对原始气象特征进行重构,然后利用长短期记忆网络分别提取两部分的时序特征,并融合所得特征输入至多层感知器,进行提前1小时的水平面总辐照度预测。实验结果表明,该方法能捕捉辐射序列的波动和突变,并考虑不同气象特征的重要程度,可有效提高短期太阳辐照度的预测精度。
Abstract
In order to improve the accuracy of short-term solar radiation forecasting, a novel forecasting method based on ICEEMDAN-LSTM and residual attention is proposed. The original radiation sequence is first decomposed into multiple modal components by the improved complete ensemble empirical modal decomposition with adaptive noise. Residual attention is introduced to reconstruct the original meteorological features at the same time. Long short-term memory（LSTM） network is then utilized to extract temporal features of the two parts, respectively. After that, temporal features of each part are concatenated as inputs of a multi-layer perception, which can generate one-hour-ahead prediction results of global horizontal irradiance. Experimental results demonstrate that the proposed method can capture the fluctuations and abrupt changes of the irradiance series and is able to consider the importance of different meteorological features for the prediction task. The proposed method is proved to be effective in improving the prediction accuracy of short-term solar irradiance.
关键词
太阳辐照度 /
预测 /
深度学习 /
ICEEMDAN /
长短期记忆网络 /
残差注意力
Key words
solar radiation /
forecasting /
deep learning /
ICEEMDAN /
long short-term memory network /
residual attention
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] TONG J L, XIE L P, FANG S X, et al.Hourly solar irradiance forecasting based on encoder-decoder model using series decomposition and dynamic error compensation[J]. Energy conversion and management, 2022, 270: 116049.
[2] ELIZABETH MICHAEL N, HASAN S, AL-DURRA A, et al.Short-term solar irradiance forecasting based on a novel Bayesian optimized deep long short-term memory neural network[J]. Applied energy, 2022, 324: 119727.
[3] 张鸿皓, 杨国华, 郑豪丰, 等. 基于多维特征分析的双层协同太阳辐照度预测[J]. 太阳能学报, 2022, 43(8): 143-149.
ZHANG H H, YANG G H, ZHENG H F, et al.Prediction of bi-layer cooperative solar radiation intensity based on multi-feature analysis[J]. Acta energiae solaris sinica, 2022, 43(8): 143-149.
[4] 臧海祥, 程礼临, 刘玲, 等. 基于数据驱动的太阳辐射估计和预测研究与展望[J]. 电力系统自动化, 2021, 45(11): 170-183.
ZANG H X, CHENG L L, LIU L, et al.Research and prospect for data-driven estimation and prediction of solar radiation[J]. Automation of electric power systems, 2021, 45(11): 170-183.
[5] 周满国, 黄艳国, 段锦锋. 基于GRU-RF模型的太阳辐照度短时预测[J]. 太阳能学报, 2022, 43(7): 166-173.
ZHOU M G, HUANG Y G, DUAN J F.Short term prediction of soral irradiance based on GRU-RF model[J]. Acta energiae solaris sinica, 2022, 43(7): 166-173.
[6] KUMARI P, TOSHNIWAL D.Long short term memory-convolutional neural network based deep hybrid approach for solar irradiance forecasting[J]. Applied energy, 2021, 295: 117061.
[7] ZANG H X, LIU L, SUN L, et al.Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations[J]. Renewable energy, 2020, 160: 26-41.
[8] 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.
[9] NGOC-LAN HUYNH A, DEO R C, ALI M, et al. Novel short-term solar radiation hybrid model: long short-term memory network integrated with robust local mean decomposition[J]. Applied energy, 2021, 298: 117193.
[10] HUANG X Q, LI Q, TAI Y H, et al.Hybrid deep neural model for hourly solar irradiance forecasting[J]. Renewable energy, 2021, 171: 1041-1060.
[11] PENG T, ZHANG C, ZHOU J Z, et al.An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting[J]. Energy, 2021, 221: 119887.
[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] GAO B X, HUANG X Q, SHI J S, et al.Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks[J]. Renewable energy, 2020, 162: 1665-1683.
[14] SIBTAIN M, LI X S, SALEEM S, et al.A multistage hybrid model ICEEMDAN-SE-VMD-RDPG for a multivariate solar irradiance forecasting[J]. IEEE access, 2021, 9: 37334-37363.
[15] COLOMINAS M A, SCHLOTTHAUER G, TORRES M E.Improved complete ensemble EMD: a suitable tool for biomedical signal processing[J]. Biomedical signal processing and control, 2014, 14: 19-29.
[16] SENGUPTA M, XIE Y, LOPEZ A, et al.The national solar radiation data base (NSRDB)[J]. Renewable and sustainable energy reviews, 2018, 89: 51-60.
PDF(1768 KB)
