The forecast for photovoltaic (PV) power generation is of great significance for the operation and control of power system. In this paper, a short-term combination forecasting model for PV power based on similar day and cross entropy theory is proposed. The main influencing factors of PV power are analyzed. From the perspective of entropy theory, considering distance entropy and grey relation entropy, a comprehensive index is proposed to select similar days. Then, the least square support vector machine (LSSVM), autoregressive and moving average (ARMA), and back propagation (BP) neural network are used to forecast PV power, respectively. The weights of three single forecasting methods are dynamically set by the cross entropy algorithm and the short-term combination forecasting model for PV power is established. The results show that this method can effectively improve the prediction accuracy of PV power and is of great significance to real-time economical dispatch.

中文翻译：

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基于相似日和交叉熵理论的短期光伏发电组合预测方法

光伏（PV）发电量预测对于电力系统的运行和控制具有重要意义。本文提出了一种基于相似日和交叉熵理论的光伏发电短期组合预测模型。分析了光伏发电的主要影响因素。从熵理论的角度，综合考虑距离熵和灰色关联熵，提出了综合指标选择相似天数。然后，分别使用最小二乘支持向量机（LSSVM）、自回归移动平均（ARMA）和反向传播（BP）神经网络对光伏功率进行预测。通过交叉熵算法动态设置三种单一预测方法的权重，建立光伏发电短期组合预测模型。