Multi-Branch ResNet-Transformer for Short-Term Spatio-Temporal Solar Irradiance Forecasting
Multi-Branch ResNet-Transformer for Short-Term Spatio-Temporal Solar Irradiance Forecasting
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DOI：
10.1109/tia.2023.3285202
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发表时间：
2023-09
影响因子：
4.4
通讯作者：
S. Ziyabari;Zhenyu Zhao;Liang Du;Saroj K. Biswas
中科院分区：
文献类型：
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作者：
S. Ziyabari;Zhenyu Zhao;Liang Du;Saroj K. Biswas
太阳能发电日益渗透到电网中，促进了对准确可靠的短期太阳辐照度预测的需求。与传统的时间序列分析技术相比，利用先进的深度学习架构的现有方法表现出了先进的性能，但通常在对相邻太阳能发电站点之间的空间相关性建模，探索长期时变模式的相似性以及减轻卷积和递归神经网络（如流行的长短期记忆（LSTM））中的过拟合问题方面遇到了缺点。为了有效但可靠地解决现有文献中的这些挑战，本文提出了一个时空框架，由多分支混合残差网络和Transformer架构（ResTrans）组成。所提出的框架已经在两组真实世界的数据上进行了测试，这些数据包含来自美国费城不同太阳能站点的17年数据，分别包括12个和18个位置。与其他混合基准架构相比，包括单分支ResTrans和多分支ResNet-LSTM（ResLSTM），单分支ResLSTM和CNN-LSTM，所提出的多分支ResTrans实现了最高的预测精度，平均RMSE为0.049（W/m2），平均MAE为0.031（W/m2），$R^{2}$系数为97%。
The increasing penetration of solar generation into power grids has promoted the need for accurate and reliable short-term solar irradiance forecasting. Existing methods utilizing advanced deep learning architectures have shown advanced performance compared to conventional time-series analytical techniques but in general encountered shortcomings in modeling spatial correlations among neighboring solar generation sites, exploring the similarity of long-term, time-varying patterns, and alleviating overfitting issues in convolutional and recurrent neural networks, such as the popular Long Short-term Memory (LSTM). To effectively but yet reliably tackle these challenges in the existing literature, this article proposes a spatio-temporal framework consisting of a multi-branch hybrid Residual network and the Transformer architecture (ResTrans). The proposed framework has been tested on two groups' real-world data containing 17 years-long data from different solar sites in Philadelphia, USA, including 12 and 18 locations, respectively. Compared to other hybrid benchmark architectures, including single-branch ResTrans and multi-branch ResNet-LSTM (ResLSTM), single-branch ResLSTM, and CNN-LSTM, the proposed multi-branch ResTrans achieves the highest forecasting accuracy with an average RMSE of 0.049 (W/m2), an average MAE of 0.031 (W/m2), and an $R^{2}$ coefficient of 97%.