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Prediction of energy consumption in the Industry 4.0 platform- solutions overview

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
For a long time, scientific and technical work has been focused on production management, which affects both the correctness of the process and the costs generated. One of the integral elements of the production process management is energy, which has an impact on the organization of work, operation of machines or production. Predicting the energy consumption of smart facilities is crucial for implementing energy-efficient management systems, the area of this problem is a key aspect of smart grids whereby loads must be planned in real time. One of the main tasks of intelligent systems is to optimize the energy demand and costs to maximize energy efficiency of the facility. According to forecasting requirements, the following article presents several approaches to prediction of energy consumption models for production engineering systems. The proposed models were adopted and analyzed in terms of their usability and were trained and validated with the use of real data collected from the electrical installation of some company using the APA IPOE system.
Rocznik
Strony
455–468
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
  • APA Group, Gliwice, Poland
autor
  • APA Group, Gliwice, Poland
  • APA Group, Gliwice, Poland
  • APA Group, Gliwice, Poland
  • Silesian University of Technology, Faculty of Mechanical Engineering, Gliwice, Poland
  • Silesian University of Technology, Faculty of Mechanical Engineering, Gliwice, Poland
Bibliografia
  • 1. Adenuga O.T., Mpofu K., Ramatsetse B.I., 2020, Exploring energy efficiency prediction method for Industry 4.0: a reconfigurable vibrating screen case study, Procedia Manufacturing, 51, 243-250.
  • 2. APAGROUP-1, “IPOE”, https://www.apagroup.pl/en/seo... (06, 2021).
  • 3. APAGROUP-2, “NAZCA4.0”, https://www.apagroup.pl/nazca-... (06,2021).
  • 4. Aslam Z., Javaid N., Ahmad A., Ahmed A., Gulfam S.M., 2020, A combined deep learning and ensemble learning methodology to avoid electricity theft in smart grids, Energies, 13, 21.
  • 5. Bonilla S.H., Silva H.R.O., da Silva M.T., Gonçalves R.F., Sacomano J.G., 2018 Industry 4.0 and sustainability implications: A scenario-based analysis of the impacts and challenges, Sustainability, 10, 10.
  • 6. Chen C.-W., Li C.-C., Lin C.-Y., 2020, Combine clustering and machine learning for enhancing the efficiency of energy baseline of chiller system, Energies, 13, 17.
  • 7. Díaz Redondo R., Vilas A., Rodríguez A., 2020, Inferring energy consumption patterns in public buildings, Conference Paper, 11, 33-39.
  • 8. Gawin Ł., 2017, Heterogeneous ensemble of specialised models – a case study in stock market recommendations, International Symposium on Methodologies for Intelligent Systems, ISMIS 2017, 728-734.
  • 9. Gościniak T., Wodarski K., 2019, Effectiveness of using the method of artificial intelligence in maintenance of ICT systems, Management Systems in Production Engineering, 27, 1, 40-45.
  • 10. Gruber F.E., 2013, Industry 4.0: a Best Practice Project of the Automotive Industry, Digital Product and Process Development Systems, Berlin, Heidelberg, 36-40.
  • 11. Leme J.V., Casaca W., Colnago M., Dias M.A., 2020, Towards assessing the electricity demand in Brazil: Data-driven analysis and ensemble learning models, Energies, 13, 6.
  • 12. Lubbe F., Maritz J., Harms T., 2020, Evaluating the potential of Gaussian process regression for solar radiation forecasting: A case study, Energies, 13, 20.
  • 13. Papalexopoulos A.D., Hesterberg T.C., 1990, A regression-based approach to short-term system load forecasting, IEEE Transactions on Power Systems, 5, 4, 1535-1547.
  • 14. Park H., Son J.-H., 2021, Machine learning techniques for THz imaging and time-domain spectroscopy, Sensors, 21, 4, 1186.
  • 15. Podder A.K., Islam S., Kumar N. M., Chand A. A., Rao P. N., Prasad K. A., Logeswaran T., Mamun K. A., 2020, Systematic categorization of optimization strategies for virtual power plants, Energies, 13, 23.
  • 16. Pollak A., Hilarowicz A., Walczak M., Gąsiorek D., 2020, A framework of action for implementation of Industry 4.0 an empirically based research, Sustainability, 12, 14.
  • 17. Prophet, 2021, Facebook Prophet, http://facebook.github.io/prop... start.html (07, 2021).
  • 18. Ptasinski W., Pollak A., Temich S., Gasiorek D., 2021, The influence of the conditio of bearings on the maintenance of production processes (in Polish), Management and Quality – Zarządzanie i Jakość, 3, 1, 60-73, ISSN 2658-2104.
  • 19. Santra A.S., Lin J.-L., 2019, Integrating long short-term memory and genetic algorithm for short-term load forecasting, Energies, 12, 11.
  • 20. Skrobek D., Krzywanski J., Sosnowski M., Kulakowska A., Zylka A., Grabowska K., Ciesielska K., Nowak W., 2020, Prediction of sorption processes using the deep learning methods (long short-term memory), Energies, 13, 24.
  • 21. Soutullo S., Giancola E., Jiménez M. J., Ferrer J.A., Sánchez M. N., 2020, How climate trends impact on the thermal performance of a typical residential building in Madrid, Energies, 13, 237.
  • 22. Temich S., Chruszczyk L., Grzechca D., 2018, Identification of the specification parameters for a voltage controlled oscillator using an artificial neural network with a genetic algorithm, Elektronika ir Elektrotechnika, 24, 6, 42-49.
  • 23. Temich S., Golonek T., Grzechca D., 2019, Design an identification function to reduce the computational resources on the testing process of an analog electronic circuit, Elektronika ir Elektrotechnika, 25, 3, 25-33.
  • 24. Tkachenko V., Kuzior A., Kwilinski A., 2019, Introduction of artificial intelligence tools into the training methods of entrepreneurship activities, Journal of Entrepreneurship Education, 22, 6.
  • 25. Wang B., Tao F., Fang X., Liu C., Liu Y., Freiheit T., 2020, Smart manufacturing and intelligent manufacturing: a comparative review, Engineering, https://doi.org/10.1016/j.eng.....
Uwagi
„Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).”
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0879fb76-7e98-47eb-bfa8-3aa9d453f598
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