Multi-objective regression modeling for natural gas prediction with ridge regression and CMARS




Ridge Regression, (C)MARS, CQP, Interior Point, Prediction of Natural Gas Consumption


Residential customers are the main users generally need a great quantity of natural gas in distribution systems, especially, in the wintry weather season since it is particularly consumed for cooking and space heating. Hence, it ought to be non-interruptible. Since distribution systems have a restricted ability for supply, reasonable planning and prediction through the whole year, especially in winter seasons, have emerged as vital. The Ridge Regression (RR) is formulated mainly to decrease collinearity results through shrinking the regression coefficients and reducing the impact in the model of variables. Conic multivariate adaptive regression splines ((C)MARS) model is constructed as an effective choice for MARS by using inverse problems, statistical learning, and multi-objective optimization theories. In this approach, the model complexity is penalized in the structure of RR and it is constructed a relaxation by utilizing continuous optimization, called Conic Quadratic Programming (CQP). In this study, CMARS and RR are applied to obtain forecasts of residential natural gas demand for local distribution companies (LDCs) that require short-term forecasts, and the model performances are compared by using some criteria. Here, our analysis shows that CMARS models outperform RR models. For one-day-ahead forecasts, CMARS yields a MAPE of about 4.8%, while the same value under RR reaches 8.5%. As the forecast horizon increases, it can be seen that the performance of the methods becomes worse, and for a forecast one week ahead, the MAPE values for CMARS and RR are 9.9% and 18.3%, respectively.


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Author Biography

Ayse Ozmen, Department of Mathematics and Statistics, University of Calgary, Canada

Ayse Özmen studied Industrial Engineering and also Mathematics and Computer Science as a double major at Çankaya University. She earned her M.S. and Ph.D. degrees in Scientific Computing at the Institute of Applied Mathematics from Middle East Technical University (METU). Her master thesis was honored by the Best Thesis Award of METU in 2012. Then her Ph.D thesis was honored by the Serhat Ozyar Award in 2016. Then, she has been in Canada for 1.5 years as a Post-doctoral Scholar at the University of Calgary, Department of Mathematics and Statistics. Her research field is on Optimization, Network Modeling, Data Science, Machine Learning, and Mathematical Modeling and Programming, Energy Modeling. Dr. Özmen has participated in many research projects from different areas and (co-)authored lots of special issues and articles.


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DOI: 10.11121/ijocta.2022.1084
Published: 2022-01-01

How to Cite

Ozmen, A. (2022). Multi-objective regression modeling for natural gas prediction with ridge regression and CMARS . An International Journal of Optimization and Control: Theories & Applications (IJOCTA), 12(1), 56–65.



Research Articles