The energy efficiency monitoring methods in industry are based on statistical modeling of energy consumption. In the present paper, the widely used method of energy efficiency monitoring “Monitoring and Targeting systems” has been considered, highlighting one of the most important issues — selection of the proper mathematical model of energy consumption. The paper gives a list of different models that can be applied in the corresponding systems. The numbers of criteria that estimate certain characteristics of the mathematical model are represented. The traditional criteria of model adequacy and the “additional” criteria, which allow estimating the model characteristics more precisely, are proposed for choosing the mathematical model of energy consumption in “Monitoring and Targeting systems”. In order to provide the comparison of different models by several criteria simultaneously, an approach based on Data Envelopment Analysis is proposed. Such approach allows providing a more accurate and reliable energy efficiency monitoring.
 F. Kreith and D. Y. Goswami, Eds., Energy management and conservation handbook (Mechanical and Aerospace Engineering Series), CRC Press, Taylor & Francis Group, 2007.
 A. Baskys, V. Nakhodov, D. Ivanko and C. Pfeiffer, “Calculation of Electrical Energy Balances of Production Systems Based on Probabilistic-Statistical Approach,” in 2015 IEEE 3rd Workshop on Advances in Inform., Electron. and Elect. Eng. (AIEEE), Riga, 2015, pp. 1–6. https://doi.org/10.1109/aieee.2015.7367293
 European Commission – Fact Sheet. (2016, June 14). The Investment Plan for Europe and Energy: making the Energy Union a reality. [Online]. Available: http://europa.eu/rapid/press-release_MEMO-16-2195_en.htm
 I. Beinarts, U. Grunde and A. Jakovics, “Distributed Multi-Sensor Real-Time Building Environmental Parameters Monitoring System with Remote Data Access,” Elect., Control and Communication Eng., vol. 7, pp. 41–46, March 2014. Available: https://doi.org/10.1515/ecce-2014-0022
 S. Hu, F. Liu, Y. He and T. Hu, “An on-line approach for energy efficiency monitoring of machine tools,” J. of Cleaner Production, vol. 27, pp. 133–140, May 2012. https://doi.org/10.1016/j.jclepro.2012.01.013
 V. Nahodov, O. Borychenko and D. Ivanko, “Monitoring of energy efficiency in energy management systems,” J. of KNUTD, vol. 6, pp. 67–77, 2013. (in Ukrainian).
 ETSU and Cheriton Technology Management Ltd. (1998). Monitoring and Targeting in large companies. Good Practice Guide 112. [Online]. Available: http://www.cibse.org/getmedia/f0fa2ac9-c4bb-4aa1-9dfd-8e1858fbbd4c/GPG112-Monitoring-and-Targeting-in-Large-Companies.pdf.aspx
 P. Jones, Getting started with Monitoring&Targeting (M&T). Fundamental Series, no. 7. pp. 29–32, 2004.
 Computer Based Monitoring And Targeting On A Hot Rolling Mill. Energy Efficiency Enquiries Bureau, ETSU, Harwell, Oxford shire, 0X11. Best Practice Program. 1992. 26 p.
 C. Dougherty, Introduction to econometrics, 2nd ed. Oxford University Press, 2002, Paperback, 424 p.
 A. A. Afifi and V. Clark, Computer-Aided Multivariate Analysis, 3rd ed. Springer Science Business Media, 1996, 456 p.
 G. A. F. Seber and C. J. Wild, Nonlinear Regression (Wiley Series in Probability and Statistics), New York: John Wiley & Sons, Inc., 1989. https://doi.org/10.1002/0471725315
 K. H. Esbensen, Multivariate Data Analysis – In Practice, 5th ed. Oslo, Norway: CAMO Software, 2002.
 M. A. Piette, S. Kinney and H. Friedman, “EMCS Time-Series Energy Data Analysis in a Large Government Office Building,” Proc. of 9th National Conf. on Building Commissioning, Cherry Hill, New Jersey, May 9–11, 2001, pp. 1–10.
 O. Y. Rodionova, K. H. Esbensen and A. L. Pomerantsev, “Application of SIC (simple interval calculation) for object status classification and outlier detection–comparison with regression approach,” J. of Chemometrics, vol. 18, issue 9, pp. 402–413, 2004. https://doi.org/10.1002/cem.885
 A. G. Ivakhnenko, G. A. Ivakhnenko, “The Review of Problems Solvable by Algorithms of the Group Method of Data Handling (GMDH),” Pattern Recognition and Image Analysis, vol. 5, no. 4, pp. 527–535, 1995.
 M. H. Beale, M. T. Hagan and H. B. Demuth. (2010). Neural Network Toolbox™ 7 User’s Guide, 1992–2011, The MathWorks, Inc.,
 J. Zhu, Ed., Data Envelopment Analysis: A Handbook of Models and Methods, Yew York: Springer, 2015, 472 p.