College of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, China.
Department of Electronic Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan.
Comput Intell Neurosci. 2021 Aug 26;2021:6638436. doi: 10.1155/2021/6638436. eCollection 2021.
When a photovoltaic (PV) system is connected to the electric power grid, the power system reliability may be exposed to a threat due to its inherent randomness and volatility. Consequently, predicting PV power generation becomes necessary for reasonable power distribution scheduling. A hybrid model based on an improved bird swarm algorithm (IBSA) with extreme learning machine (ELM) algorithm, i.e., IBSAELM, was developed in this study for better prediction of the short-term PV output power. The IBSA model was initially used to optimize the hidden layer threshold and input weight of the ELM model. Further, the obtained optimal parameters were input into the ELM model for predicting short-term PV power. The results revealed that the IBSAELM model is superior in terms of the prediction accuracy compared to existing methods, such as support vector machine (SVM), back propagation neural network (BP), Gaussian process regression (GPR), and bird swarm algorithm with extreme learning machine (BSAELM) models. Accordingly, it achieved great benefits in terms of the utilization efficiency of whole power generation. Furthermore, the stability of the power grid was well maintained, resulting in balanced power generation, transmission, and electricity consumption.
当光伏（PV）系统与电网连接时，由于其固有的随机性和波动性，电力系统的可靠性可能会受到威胁。因此，为了进行合理的配电调度，有必要对光伏发电进行预测。本研究提出了一种基于改进的鸟群算法（IBSA）和极限学习机（ELM）算法的混合模型，即 IBSAELM，用于更好地预测短期光伏输出功率。首先，IBSA 模型用于优化 ELM 模型的隐藏层阈值和输入权重。然后，将获得的最优参数输入到 ELM 模型中，以预测短期光伏功率。结果表明，与支持向量机（SVM）、反向传播神经网络（BP）、高斯过程回归（GPR）和鸟群算法与极限学习机（BSAELM）模型等现有方法相比，IBSAELM 模型在预测精度方面具有优势。因此，它在整个发电的利用效率方面带来了巨大的效益。此外，还可以很好地维持电网的稳定性，实现均衡发电、输电和用电。
