基于现代统计学模型的非线性电力负荷预测

Nonlinear power load prediction based on modern statistical model

传统的非线性电力负荷预测方法电力负荷信号收集精准度差,预测准确率低。为此,文中基于现代统计学研究一种新的非线性电力负荷预测模型。首先,进行基础数据测量,实现对初始数据的精准查找操作,进一步调整数据状态,选取相关变量进行预测数据预处理操作,根据数据基础信息状况进行内部机制调节,不断优化基础性操作,完善信息结构,并将处理后的数据作为操作标准进行预测模型的构建,结合构建算法进行数据分析,综合内部匹配准则,实现整体性构建操作,达到对非线性电力负荷预测的研究目的。实验结果表明,该预测方法在一定程度上优化了系统内部操作机制,调整了数据的基础状态,缩减了系统操作所需时间,提升了系统运行效率,整合了数据信息,提高了预测的准确率。

Traditional non⁃linear power load prediction methods have poor collection accuracy of power load signal and low prediction accuracy.Therefore,a new non⁃linear power load prediction model was researched on the basis of modern statistics.The basic data measurement is performed to realize the accurate search operation of the initial data.The data status is further adjusted to select the relevant variables for the operation of the prediction data preprocessing.The internal mechanism is adjusted according to the data foundation information status for continuous optimization of the basic operations and improvement of the information structure.The processed data are taken as the operating standard to construct the prediction model.The data analysis is performed in combination with the construction algorithm.The overall construction operation is achieved according to the internal matching criteria to realize the research purpose of nonlinear power load prediction.The experimental results show that the prediction method can optimize the internal operation mechanism of the system to a certain extent,adjust the basic state of the data,shorten the time of the system operation,improve operation efficiency of the system,integrate data information,and improve the accuracy of the prediction.