工作面多变量瓦斯体积分数时间序列预测模型

为了有效分析煤矿瓦斯监测数据以实现较准确的工作面瓦斯体积分数预测,基于贝叶斯网络方法、混沌相空间重构技术与高斯过程回归模型,研究了瓦斯体积分数时间序列分析与预测的方法.应用贝叶斯网络方法提取与工作面瓦斯体积分数时间序列有较强关联特征的样本数据集,构建了多变量瓦斯体积分数时间序列预测模型；采用混沌相空间重构技术来实现多变量瓦斯体积分数时间序列样本空间重构；应用高斯过程回归模型进行工作面多变量瓦斯体积分数预测,以预测值及其置信区间来表达对工作面未来瓦斯体积分数动态变化的预测.实例分析表明:应用该方法得到的预测结果,其预测精度较单变量瓦斯体积分数时间序列预测有较大提升,并且预测区间在相同置信水平下达到了最优,能够较好的反映工作面瓦斯体积分数的动态变化状况.

Prediction Model of Gas Concentration Around Working Face Using Multivariate Time Series

For the purpose to achieve more accurate prediction of gas concentration around working face through effective analysis of gas measuring data in mines,based on Bayesian network method,chaotic phase space reconstructive technology,and Gaussian process regression model,we studied the prediction method for gas concentration by time series analysis in this paper.By applying Bayesian network method,the sample data sets which have strong relative features with gas concentration around face in time series were extracted to construct the prediction model of multivariate time series.Additionally,the sample space of gas concentration in multivariate time series was reconstructed by using chaotic phase space reconstructive technology.Furthermore,the gas concentration prediction around the face was carried out by using Gaussian process regression model,namely,the prediction values and the corresponding confidence intervals were used to describe the dynamic variation of gas concentration around the face.The case study shows that the prediction results of this method have much higher accuracy than that of univariate method,and the prediction interval is optimal in the same confidence level,which means it can better reflect the dynamic variation of gas concentration around the working face.