Input variable selection for the statistical prediction model on energy consumption of products pipelines
Selecting an appropriate input vector is a critical issue in forecasting.For predicting the daily energy consumption of multiproduct pipelines,the input vector design is usually based on engineering experience,and the number of the traditional input variables is large.However,such a forecast model based on the traditional input variables needs more computational resources and longer training time.In order to prune the number of input variables,the correlation coefficient and partial correlation coefficient are introduced to measure the correlation between input variables and the output variable.In a case study involving a Chinese products pipeline,a new input vector,which contains 5 input variables,are identified from the traditional 15 input variables through the correlation analysis.To verify the rationality of the new vector,an ANN model based on the new vector is developed to forecast the daily energy consumption,and its forecasted values are compared to the values of the ANN model based on the traditional input vector and actual values.The results show that the ANN model based on the new vector has higher prediction accuracy,indicating that more parsimonious set of input variables can be used in daily energy consumption forecast of products pipeline without sacrificing the accuracy of the forecast.
Products pipeline Energy consumption Prediction Forecasting model
Chunlei Zeng Changchun Wu Peng Li Bin Zhang
Beijing Key Laboratory of Urban Oil and Gas Distribution Technology,China University of Petroleum-Be Beijing Key Laboratory of Urban Oil and Gas Distribution Technology,China University of Petroleum-Be China National Petroleum Corporation Planning Department,Beijing,China
国际会议
桂林
英文
1-7
2013-07-16（万方平台首次上网日期，不代表论文的发表时间）