Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou 310018, China.
Department of the Built Environment, College of Design and Engineering, National University of Singapore, 4 Architecture Drive, Singapore 117566, Singapore.
Sensors (Basel). 2023 Mar 3;23(5):2799. doi: 10.3390/s23052799.
With the rapid development of solar energy plants in recent years, the accurate prediction of solar power generation has become an important and challenging problem in modern intelligent grid systems. To improve the forecasting accuracy of solar energy generation, an effective and robust decomposition-integration method for two-channel solar irradiance forecasting is proposed in this study, which uses complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), a Wasserstein generative adversarial network (WGAN), and a long short-term memory network (LSTM). The proposed method consists of three essential stages. First, the solar output signal is divided into several relatively simple subsequences using the CEEMDAN method, which has noticeable frequency differences. Second, high and low-frequency subsequences are predicted using the WGAN and LSTM models, respectively. Last, the predicted values of each component are integrated to obtain the final prediction results. The developed model uses data decomposition technology, together with advanced machine learning (ML) and deep learning (DL) models to identify the appropriate dependencies and network topology. The experiments show that compared with many traditional prediction methods and decomposition-integration models, the developed model can produce accurate solar output prediction results under different evaluation criteria. Compared to the suboptimal model, the MAEs, MAPEs, and RMSEs of the four seasons decreased by 3.51%, 6.11%, and 2.25%, respectively.
近年来，随着太阳能发电厂的快速发展，准确预测太阳能发电已成为现代智能电网系统中的一个重要且具有挑战性的问题。为了提高太阳能发电的预测精度，本研究提出了一种用于双通道太阳辐照度预测的有效且稳健的分解-集成方法，该方法使用完全集合经验模态分解自适应噪声（CEEMDAN）、Wasserstein 生成对抗网络（WGAN）和长短时记忆网络（LSTM）。所提出的方法包括三个基本阶段。首先，使用 CEEMDAN 方法将太阳能输出信号分为几个相对简单的子序列，这些子序列具有明显的频率差异。其次，使用 WGAN 和 LSTM 模型分别对高低频子序列进行预测。最后，将每个分量的预测值进行集成，以获得最终的预测结果。所开发的模型使用数据分解技术，结合先进的机器学习（ML）和深度学习（DL）模型来识别适当的依赖关系和网络拓扑。实验表明，与许多传统预测方法和分解-集成模型相比，所开发的模型可以在不同的评估标准下产生准确的太阳能输出预测结果。与次优模型相比，四个季节的 MAEs、MAPEs 和 RMSEs 分别降低了 3.51%、6.11%和 2.25%。
