Accurate solar irradiance prediction plays an important role in renewable energy systems. Based on time series analysis, a serially connected multi-reservoir echo state network (MR-ESN) is developed to predict solar irradiance. MR-ESN is a fast yet efficient approach, which makes use of the high efficiency of ESN and the advantages of deep learning. MR-ESN consists of multiple reservoirs in series, which are responsible for encoding the input signals into a richer state representation. The time series analysis is adopted to provide more appropriate input and output for MR-ESN. Various prediction horizons including one-hour-ahead and multi-hour-ahead prediction are conducted, respectively. The effect of reservoir layer number on the MR-ESN performance is explored in detail. Three internal qualitative indicators are adopted to investigate the performance differences of MR-ESN, i.e., probability distribution, correlation analysis, and principal component analysis (PCA) of network states. Simulation results demonstrate that MR-ESN outperforms than traditional ESN, backpropagation (BP) and Elman neural networks.

准确的太阳辐照度预测在可再生能源系统中起着重要作用。基于时间序列分析，开发了一个串联的多水库回波状态网络（MR-ESN）来预测太阳辐照度。MR-ESN是一种快速而有效的方法，它利用了ESN的高效性和深度学习的优势。MR-ESN由多个串联的储存器组成，这些储存器负责将输入信号编码为更丰富的状态表示形式。采用时间序列分析为MR-ESN提供更合适的输入和输出。分别进行了包括一个小时前预测和多个小时前预测在内的各种预测范围。详细探讨了储层层数对MR-ESN性能的影响。采用三个内部定性指标来研究MR-ESN的性能差异，即网络状态的概率分布，相关性分析和主成分分析（PCA）。仿真结果表明，MR-ESN的性能优于传统的ESN，反向传播（BP）和Elman神经网络。