Accurate and reliable solar radiation forecasting is of great significance for the management and utilization of solar energy. This study proposes a deep learning model based on Bi-directional long short-term memory (BiLSTM), sine cosine algorithm (SCA) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for solar radiation forecasting. Firstly, the CEEMDAN is applied to decompose the stochastic historical time series into certain periodic intrinsic mode functions (IMFs) and a residual. Secondly, the significant antecedent solar radiation patterns of the decomposed sub-modes are identified via two statistical techniques, namely, the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Thirdly, all the sub-modes are forecasted using the BiLSTM model, and the parameters of the BiLSTM model are optimized using the SCA algorithm. Finally, the forecasted sub-modes are aggregated to generate the final forecasting result. The accuracy of the proposed deep learning model is investigated by applying it in forecasting hourly solar radiation of four real-world datasets over multi-step horizons. Comparative experiments with other seven models demonstrate the effectiveness of the integrated model, the CEEMDAN technique and the SCA algorithm, respectively. The proposed model can obtain higher prediction accuracy than the existing models for all datasets and forecasting horizons.

准确可靠的太阳辐射预报对太阳能的管理和利用具有重要意义。本研究提出了一种基于双向长短期记忆（BiLSTM），正弦余弦算法（SCA）和具有自适应噪声的完整集成经验模式分解（CEEMDAN）的深度学习模型，用于太阳辐射预测。首先，将CEEMDAN应用于将随机历史时间序列分解为某些周期性固有模式函数（IMF）和残差。其次，通过两种统计技术，即自相关函数（ACF）和部分自相关函数（PACF），确定了已分解子模式的重要先行太阳辐射方向图。第三，使用BiLSTM模型预测所有子模式，使用SCA算法优化BiLSTM模型的参数。最后，将预测的子模式汇总以生成最终的预测结果。通过将其应用于预测多步地平线上的四个真实世界数据集的每小时太阳辐射，来研究所提出的深度学习模型的准确性。与其他七个模型的比较实验分别证明了集成模型，CEEMDAN技术和SCA算法的有效性。对于所有数据集和预测范围，所提出的模型可以获得比现有模型更高的预测精度。通过将其应用于预测多步地平线上的四个真实世界数据集的每小时太阳辐射，来研究所提出的深度学习模型的准确性。与其他七个模型的比较实验分别证明了集成模型，CEEMDAN技术和SCA算法的有效性。对于所有数据集和预测范围，所提出的模型可以获得比现有模型更高的预测精度。通过将其应用于预测多步地平线上的四个真实世界数据集的每小时太阳辐射，来研究所提出的深度学习模型的准确性。与其他七个模型的比较实验分别证明了集成模型，CEEMDAN技术和SCA算法的有效性。对于所有数据集和预测范围，所提出的模型可以获得比现有模型更高的预测精度。