基于JITL的电量预测高斯软测量建模研究

Research on Gaussian soft sensing modeling of electricity prediction based on JITL

为了解决工业过程受本身结构特征、外界因素等影响而存在严重的非线性和时变性等问题,本文提出了一种基于输入输出综合性相似度指标的即时学习高斯过程软测量建模方法。在该方法中,将样本数据进行归一化处理,首先利用传统的基于距离和角度的相似度指标分别对样本输入输出变量进行相似度计算,进而对相似度进行综合,最后选择出最终的相关样本集,建立高斯过程回归软测量模型,将所提基于输入输出相似度指标的即时学习高斯工程软测量模型应用于城市日用电量数据的预测。研究结果表明,所提出的软测量建模方法可以实现对日用电量数据的高精度预测且预测结果具有较小的误差。因此可表明该方法可在电量预测中具有一定的应用可靠性,可以在电力市场预测分析中得到广泛的应用。

In order to solve the serious non-linearity and time-varying problems of industrial process affected by its own structural characteristics and external factors,a real-time learning Gaussian process soft sensor modeling method based on the comprehensive similarity index of input and output is proposed. In this method,the sample data are normalized. Firstly,the traditional similarity indices based on distance and angle are used to calculate the similarity of input and output variables of samples respectively,and then the similarity is synthesized. Finally,the final relevant sample set is selected and the soft sensor model of Gaussian process regression is established. The real-time learning of Gaussian process soft sensor model based on the similarity indices of input and output is proposed. The engineering soft-sensing model is applied to the prediction of urban daily electricity consumption data. The results show that the proposed soft sensing modeling method can achieve high accuracy prediction and the prediction results have small errors. Therefore,it can be shown that this method has certain application reliability in electricity forecasting and can be widely used in power market forecasting and analysis.