微电网光伏发电的Adaboost天气聚类超短期预测方法

微电网光伏发电预测精度与天气状态呈高度相关性,非晴空条件下气象因素的随机波动使得超短期预测精度较低。对此,文中提出一种改进Adaboost天气聚类和马尔可夫链的组合预测方法。首先采用滑动平均法提取辐照度特征变量,设计并训练Adaboost改进的K近邻(KNN)分类器,实现历史样本的分类;为进一步提高多云和阴雨天的预测精度,引入天气类型衰减系数对Hottel太阳辐射模型进行校正,形成完整描述各天气类型的辐照度基准模型;建立多阶加权马尔可夫链模型输出辐照度预测值;最后由光电转换模型实现间隔5 min的微电网光伏超短期预测。仿真结果表明,所述预测方法提高了各天气类型下的预测精度,对提高微电网经济调度水平具有重要意义。

Ultra-short-term Photovoltaic Power Forecasting in Microgrid Based on Adaboost Clustering

The accuracy of photovoltaic(PV)power generation prediction in the microgrid has high relativity with the weather condition. Under cloudy and rainy conditions, random fluctuations of meteorological factors result in low precision of the ultra-short-term power prediction. For this reason, a modified model based on combination of Adaboost clustering and Markov chain is proposed. First, an improved K-nearest neighbor(KNN)classifier is trained with the characteristic variables extracted from solar radiation using the moving average method. To improve the prediction accuracy of cloudy and rainy days, the attenuation coefficient of solar radiation is introduced to modify the Hottel model. A weighted Markov chain model is developed to predict the microgrid PV generation subsequently. The simulation results indicate that the proposed model can appreciably improve the precision of power prediction under different weather conditions and is of great significance to real-time economical dispatch.