【摘要】 光伏出力受气象因素影响大,未计及气象因素的光伏出力预测结果精度不高;将多种气象数据直接作为模型输入增加了预测模型的维度;BP神经网络具有很强的线性映射能力,被广泛应用于光伏出力预测中,但是单一的BP预测模型预测误差较大。针对这些问题,提出一种基于模糊聚类和组合预测算法的方法进行光伏出力短期预测。通过pearson相关系数定量分析光伏出力与太阳辐照度、大气浑浊度、相对湿度等气象因素之间的相关性,将这三类气象数据作为聚类特征向量并通过改进的模糊C均值聚类(IFCM)算法将光伏出力历史数据和待预测日数据聚类成三类,然后选取待预测日所属类别的历史数据和待预测日的温度数据并利用遗传膜优化BP神经网络预测模型(GAPS-BP)进行预测。最后,通过实际案例仿真,验证了所提模型和优化算法的有效性,表明所提方法能够有效降低模型输入维度并提高预测精度。更多还原

【Abstract】 This paper proposes a short-term prediction method for photovoltaic power output based on fuzzy clustering and combination prediction algorithm. The correlation between the photovoltaic output and the meteorological factors(such as solar irradiance, atmospheric turbidity, relative humidity) is quantitatively analyzed by pearson correlation coefficient. These three types of meteorological data are used as clustering feature vectors. The PV output history data and the data to be predicted are divided three categories by improved fuzzy C-means clustering(IFCM) algorithm. Then the historical data of the category to be predicted and the temperature data are selected and the genetic film to optimize the BP neural network Model(GAPS-BP) is used to predict. Finally, the simulation on an actual case shows that the effectiveness of the proposed model and the optimization algorithm. The proposed method can effectively reduce the model input dimension and improve the prediction accuracy.更多还原