Energy Consumption Monitoring with Evaluation of Hidden Energy Losses
Keywords:monitoring, energy consumption, precedents, energy losses
This article presents a computational method for monitoring the energy consumption of technological systems with the assessment of their hidden energy losses caused by erroneous actions of personnel or equipment failures. Herewith, energy losses are calculated as the difference between the actual energy consumed and the minimum energy required to conduct the process in all operating modes. The minimum required energy is determined by the machine learning method based on stationary consumption precedents. Two approaches to the implementation of energy consumption monitoring with the assessment of hidden energy losses are considered – hardware and software. The hardware approach is based on the preliminary definition of normative, or minimum specific energy consumption in each technological mode. The software approach is based on the modeling of stationary areas of energy consumption in the form of precedents and their further analysis in the space of influential technological parameters. The paper notes the advantages and disadvantages of the proposed monitoring method, it is emphasized that the method is able to work with both linear and non-linear functions of energy dependence on the parameters of the technological process. It is noted in the paper that the advantage of the proposed method is the automated construction of the minimum energy function.
A. V. Prakhovnik, O. M. Zakladny, O. O. Zakladny, “Functional diagnostics of energy efficiency of electromechanical systems with induction motors,” Electrical and Computer Systems, no. 3, pp. 375-376, 2011. (in Ukrainian)
J. L. Pellegrino, N. Margolis, M. Justiniano, M. Miller, A. Thedki, Energy Use, Loss and Opportunities Analysis: U.S. Manufacturing and Mining, Prepared by Energetics, Incorporated and E3M, Incorporated for the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Industrial Technologies Program, 2004, 165 р. https://doi.org/10.2172/1218707.
S. Emec, J. Krüger, G. Seliger, “Online fault-monitoring in machine tools based on energy consumption analysis and non-invasive data acquisition for improved resource-efficiency,” Proceedings of the 13th Global Conference on Sustainable Manufacturing, Procedia CIRP 40, 2016, pp. 236–243. https://doi.org/10.1016/j.procir.2016.01.111.
Schneider Electric. EcoStruxure™ Power Monitoring Expert. [Online]. Available at: https://www.se.com/ua/ru/product-range-presentation/61280-ecostruxure%E2%84%A2-power-monitoring-expert-8
J. W. Woolard, D. J. Fong, P. L. Dell’Era, K. E. Gipson, Energy Management System and Method, Patent US 6178362 B1. [Online]. Available at: https://patents.google.com/patent/US6178362B1/en
M. Trejo-Perea, G. J. Ríos Morenoa, A. Castañeda-Miranda, D. Vargas-Vázqueza, R. V. Carrillo-Serranoa, G. Herrera-Ruiza, “Development of a real time energy monitoring platform user-friendly for buildings,” Procedia Technology, no. 7, pp. 238–247, 2013. https://doi.org/10.1016/j.protcy.2013.04.030.
Siemens, SIMATIC Energy Management. [Online]. Available at: https://new.siemens.com/global/en/products/automation/industry-software/automation-software/energymanagement/simatic-energy-manager-pro.html
A. Vijayaraghavana, D. Dornfeld, “Automated energy monitoring of machine tools,” CIRP Annals – Manufacturing Technology, vol. 59, pp. 21–24, 2010. https://doi.org/10.1016/j.cirp.2010.03.042.
G. Marques, R. Pitarma, “Monitoring energy consumption system to improve energy efficiency,” Proceedings of the World Conference on Information Systems and Technologies, 2017. https://doi.org/10.1007/978-3-319-56538-5_1.
T. S. Tung, N. A. G. Guevarra, F. A. Inestroza, System for Monitoring the Energy Efficiency of Technology Components: Patent US 8,521,476 B2 Date of Patent: Aug. 27, 2013.
P. C. Priarone, M. Robiglio, L. Settineri, V. Tebaldo, “Modelling of specific energy requirements in machining as a function of tool and lubricoolant usage,” CIRP Annals, vol. 65, issue 1, pp. 25-28, 2016. https://doi.org/10.1016/j.cirp.2016.04.108.
S. Hu, F. Liu, Y. He, T. Hu, “An on-line approach for energy efficiency monitoring of machine tools,” Journal of Cleaner Production, vol. 27, pp. 133-140, 2012. https://doi.org/10.1016/j.jclepro.2012.01.013.
Efficiency Direct, Energy Monitoring and Targeting [Online]. Available at: https://efficiency-direct.co.uk/services/energy-monitoring-and-targeting/
R. E. Castro, F. B. Líbano, L. F. Chaves, J. G. Hermes, “Automated energy monitoring and targeting system ISO50001 compatible framework,” Proceedings of the International Conference on Renewable Energy Research and Applications (ICRERA), 2013, pp. 298. https://doi.org/10.1109/ICRERA.2013.6749769.
Yu. G. Kutsan, B. M. Pleskach, The Method of Energy Loss Control in the Technological Process, Patent of Ukraine, № 124346, Bull. №7, April 10, 2018. (in Ukrainian)
D. R. Cox, H. D. Miller, The Theory of Stochastic Processes: Methuen, 1965, London, 398 p.
N. Bhatia, Vandana, “Survey of nearest neighbor techniques,” International Journal of Computer Science and Information Security, vol. 8, no. 2, рp. 302-305, 2010.
Ch. Lawson, R. Henson, Numerical Solution of Problems by the Method of Least Squares, Cambridge University Press, 1995, 337 p.
O. M. Bogdanov, B. M. Pleskach, “Information technology for energy efficiency monitoring of technological systems,” Artificial Intelligence, no. 1-2, pp. 60-69, 2019. (in Ukrainian) https://doi.org/10.15407/jai2019.01-02.060.
J. S. Bendat, A. G. Piersol, Random Data: Analysis and Measurement Procedures, 4th Edition, Wiley, 2011, 640 p. https://doi.org/10.1002/9781118032428.
І. V. Stetsenko, Ya. S. Bederak, “Construction of multifactor mathematical models of energy consumption in chemical production,” Energy saving. Energy. Energy audit, no. 7, pp. 41-48, 2013. (in Ukrainian)
S. O. M. Kamel, S. A. Elhamayed, “Mitigating the impact of IoT routing attacks on power consumption in IoT healthcare environment using convolutional neural network,” International Journal of Computer Network and Information Security (IJCNIS), vol. 12, no. 4, pp. 11-29, 2020. https://doi.org/10.5815/ijcnis.2020.04.02.
D. S. Jayalakshmi, D. Hemanand, G. Muthu Kumar, M. Madhu Rani, “An efficient route failure detection mechanism with energy efficient routing (EER) protocol in MANET,” International Journal of Computer Network and Information Security (IJCNIS), vol. 13, no. 2, pp. 16-28, 2021. https://doi.org/10.5815/ijcnis.2021.02.02.
V. Moskalenko, M. Zaretskyi, A. Moskalenko, A. Korobov, Y. Kovalskyi, “Multi-stage deep learning method with self-supervised pretraining for sewer pipe defects classification,” Radioelectronic and Computer Systems, no. 4(100), pp. 71-81, 2021. https://doi.org/10.32620/reks.2021.4.06. (in Ukrainian)
V. P. Oliinyk, D. V. Telichko, V. P. Oliinyk, “Influence of energy loss of implant with wireless power supply on the thermal state of the body,” Radioelectronic and Computer Systems, 2021, no. 3(99), pp. 114-124, 2021. https://doi.org/10.32620/reks.2021.3.09. (in Ukrainian)
B. M., Pleskach, “Precedent support for decision-making in energy management,” Artificial Intelligence Scientific Journal, vol. 25, issue 2, pp. 53-60, 2020. https://doi.org/10.15407/jai2020.02.053.
How to Cite
LicenseInternational Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:
• Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
• Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
• Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.