基于组合优化LOWESS的电能量数据缺失处理方法

摘要

针对实际电能量数据的统计分布特性,考虑到均值替代等通常方法对电能量数据缺失的处理效果欠佳,LOWESS模型的估计偏差受限于其给定的窗宽和拟合阶数,提出一种基于预测误差最小化的参数组合优化LOWESS回归模型的电能量数据缺失自动处理方法,通过对比固定窗口和阶数在非平稳的电能量数据上的预测效果,研究参数组合优化LOWESS模型的准确性、适应性以及相对优势性.通过实际数据验证,此模型能适应电能量数据不同数据分布、不同缺失比例等情况,预测准确率高,具有一定的实用参考价值.%According to the statistical distribution characteristics of the actual electric energy data, and considering the treatment effect of mean substitution method on energy loss data is usually not satisfactory, the estimation error of LOWESS model is limited by its given window width and fitting order, a LOWESS regression model of the electric energy data deletion optimization based on prediction error minimization parameters automatic processing method is proposed in this paper.By comparing the fixed window and order number in the non-stationarity of the electric energy data on the prediction effect and study the accuracy, adaptability and comparative advantage of parameters optimiza-tion LOWESS model.Through the actual data validation, the model can adapt to different data distribution of electric energy data, different loss ratio and so on, and the prediction accuracy is high, which has certain practical reference value.