The volatility and intermittency of solar energy seriously restrict the development of the photovoltaic (PV) industry. Accurate forecast of short-term PV power generation is essential for the optimal balance and dispatch of power plants in the smart grid. This article presents a machine learning approach for analyzing the volt-ampere characteristics and influential factors on PV data. A correlation analysis is employed to discover some hidden characteristic variables. Then, an adaptive ensemble method with stochastic configuration networks as base models (AE-SCN) is proposed to construct the PV prediction model, which integrates bagging and adaptive weighted data fusion algorithms. Compared with the original SCN, SCN ensemble (SCNE) and random vector functional-link network (RVFLN), linear regression model, random forest model and autoregressive integrated moving average (ARMA) model, AE-SCN performs favorably in the terms of the prediction accuracy.

中文翻译：

---

具有自适应随机配置网络集成的短期光伏功率预测

太阳能的波动性和间歇性严重制约了光伏（PV）产业的发展。准确预测短期光伏发电量对于智能电网中电站的优化平衡和调度至关重要。本文介绍了一种机器学习方法，用于分析光伏数据的伏安特性和影响因素。相关分析用于发现一些隐藏的特征变量。然后，提出了一种以随机配置网络为基础模型的自适应集成方法（AE-SCN）来构建PV预测模型，该模型集成了bagging和自适应加权数据融合算法。与原始 SCN、SCN 集成（SCNE）和随机向量功能链接网络（RVFLN）相比，线性回归模型，