Е-Публикации
Технически университет - София

Abstract: In this article, the application of virtual reality technology for the realistic and immersive visualization of various tasks and scenarios in fields such as power engineering and fire safety has been examined in order to help prepare students and professional electrical engineers with electrical safety, the operation of electrical substations, potential emergencies, injury prevention, fire safety, and others. Additionally, the use of machine learning algorithms to guide evacuations from hazardous environments, fault prevention, fire prediction, and discovery of conductive materials has been examined. The most frequently used algorithms in these areas have also been described and summarized, and conclusions have been made about the combined advantages of using VR and ML algorithms. Finally, the needs, contributions, and challenges of using machine learning in virtual reality projects have been examined.

References

1. Gutiérrez A. M.A. Vexo F. Thalmann D. Stepping into Virtual Reality Springer Nature Switzerland Cham, Switzerland 2023 10.1007/978-3-031-36487-7
2. Virtual Reality System—An Overview|ScienceDirect Topics Available online: https://www.sciencedirect.com/topics/computer-science/virtual-reality-system (accessed on 6 May 2025)
3. Architecture of Virtual Reality System-Twin Reality Available online: https://twinreality.in/architecture-of-virtual-reality-system/ (accessed on 6 May 2025)
4. Sindu I.G.P. Hartati R.S. Sudarma M. Gunantara N. Systematic literature review of machine learning in virtual reality and augmented reality J. Nas. Pendidik. Tek. Inform. JANAPATI 2023 12 108 118 10.23887/janapati.v12i1.60126
5. Le H. Nguyen M. Yan W.Q. Nguyen H. Augmented Reality and Machine Learning Incorporation Using YOLOv3 and ARKit Appl. Sci. 2021 11 6006 10.3390/app11136006
6. Jiang H. Padebettu R.R. Sakamoto K. Bastani B. Architecture of Integrated Machine Learning in Low Latency Mobile VR Graphics Pipeline SIGGRAPH Asia 2019 Technical Briefs, in SA ‘19 Association for Computing Machinery New York, NY, USA 2019 41 44 10.1145/3355088.3365154
7. FileCloud T. The Limitations of Machine Learning in an Enterprise Setting FileCloud blog Available online: https://www.filecloud.com/blog/2018/06/top-5-limitations-of-machine-learning-in-an-enterprise-setting/ (accessed on 5 May 2025)
8. Li Y. Wang Y. Liu Q. Bi C. Jiang X. Sun S. Incremental semi-supervised learning on streaming data Pattern Recognit. 2019 88 383 396 10.1016/j.patcog.2018.11.006
9. What Is Transfer Learning?|IBM Available online: https://www.ibm.com/think/topics/transfer-learning (accessed on 5 May 2025)
10. Alsheikhy A.A. Shawly T. Said Y.E. Ahmed H.E. Alazzam M.B. Developing machine learning models for personalized game-based stroke rehabilitation therapy in virtual reality Alex. Eng. J. 2025 121 358 369 10.1016/j.aej.2025.02.080
11. Herne R. Shiratuddin M.F. Rai S. Blacker D. Laga H. Improving Engagement of Stroke Survivors Using Desktop Virtual Reality-Based Serious Games for Upper Limb Rehabilitation: A Multiple Case Study IEEE Access 2022 10 46354 46371 10.1109/ACCESS.2022.3169286
12. Daş F. Elmas E.T. Bucak İ.Ö. Innovative Use of Machine Learning-Aided Virtual Reality and Natural Language Processing Technologies in Dyslexia Diagnosis and Treatment Phases Digital Frontiers-Healthcare, Education, and Society in the Metaverse Era IntechOpen Rijeka, Croatia 2024 10.5772/intechopen.1006621
13. Fathy F. Mansour Y. Sabry H. Refat M. Wagdy A. Virtual reality and machine learning for predicting visual attention in a daylit exhibition space: A proof of concept Ain Shams Eng. J. 2023 14 102098 10.1016/j.asej.2022.102098
14. Covaciu F. Pisla A. Iordan A.-E. Development of a Virtual Reality Simulator for an Intelligent Robotic System Used in Ankle Rehabilitation Sensors 2021 21 1537 10.3390/s21041537
15. Towards AI-Architecture Liberty: A Comprehensive Survey on Design and Generation of Virtual Architecture by Deep Learning Available online: https://arxiv.org/html/2305.00510v4 (accessed on 6 May 2025)
16. Galvan-Bobadilla I. Ayala-Garcia A. Rodriguez-Gallegos E. Arroyo-Figueroa G. Virtual reality training system for the maintenance of underground lines in power distribution system Proceedings of the Third International Conference on Innovative Computing Technology (INTECH 2013) London, UK 29–31 August 2013 IEEE London, UK 2013 199 204 10.1109/INTECH.2013.6653713
17. Giraldo J.S. Kok K. Paterakis N.G. A structured review on game-based learning applied to electrical power and energy engineering Comp Applic Eng. 2024 32 e22686 10.1002/cae.22686
18. Lopez J.M.G. Betancourt R.O.J. Arredondo J.M.R. Laureano E.V. Haro F.R. Incorporating Virtual Reality into the Teaching and Training of Grid-Tie Photovoltaic Power Plants Design Appl. Sci. 2019 9 4480 10.3390/app9214480
19. Loch F. Koltun G. Karaseva V. Pantförder D. Vogel-Heuser B. Model-based training of manual procedures in automated production systems Mechatronics 2018 55 212 223 10.1016/j.mechatronics.2018.05.010
20. Andaluz V.H. Pérez J.A. Carvajal C.P. Ortiz J.S. Virtual environment for teaching and learning robotics applied to industrial processes Augmented Reality, Virtual Reality, and Computer Graphics, Proceedings of the 6th International Conference, AVR 2019, Santa Maria al Bagno, Italy, 24–27 June 2019 Proceedings, Part II 6 Springer International Publishing Cham, Switzerland 2019 442 455 10.1007/978-3-030-25999-0_36
21. Fuller A. Fan Z. Day C. Barlow C. Digital Twin: Enabling Technologies, Challenges and Open Research IEEE Access 2020 8 108952 108971 10.1109/ACCESS.2020.2998358
22. Ilie R. Shaffer E. D’Angelo C.M. Cermak D. Lin M.Y. Chen H. Virtual reality laboratory experiences for electricity and magnetism courses Proceedings of the 2021 ASEE Virtual Annual Conference Content Access Virtual 2021 July 26
23. Pérez S.S. Lopez J.M.G. Barba M.A.V. Betancou R.O.J. Solís J.M. Ornelas J.L.R. García G.I.R. Haro F.R. On the Use of Augmented Reality to Reinforce the Learning of Power Electronics for Beginners Electronics 2022 11 302 10.3390/electronics11030302
24. Morelot S. Garrigou A. Dedieu J. NKaoua B. Virtual reality for fire safety training: Influence of immersion and sense of presence on conceptual and procedural acquisition Comput. Educ. 2021 166 104145 10.1016/j.compedu.2021.104145
25. Digital Engineering and Magic, VR Electrical Safety Training/OSHA VR Training Available online: https://www.youtube.com/watch?v=D4LarinRBFA (accessed on 10 May 2025)
26. AAT Training hub, Training For Electrical Safety Available online: https://aattraininghub.com/services/vr-developersingapore/technology-vr-ar-immersive-singapore-5/ (accessed on 10 May 2025)
27. Lima G.F.M. Cardoso A. Lamounier E.A. Jr. Macedo J.R. Jr. Development of a Virtual Reality Environment of Electric Power Substations for Quality of Service Improvement Available online: https://www.academia.edu/47180297/Development_of_a_Virtual_Reality_environment_of_electric_power_substations_for_quality_of_service_improvement (accessed on 10 May 2025)
28. E-Spaces, Electrical Substation VR Simulator-E-Spaces Available online: https://e-spaces.com/electrical-substation-vr-simulator/ (accessed on 10 May 2025)
29. Greenwald S.W. Corning W. McDowell G. Maes P. Belcher J. ElectroVR: An Electrostatic Playground for Collaborative, Simulation-Based Exploratory Learning in Immersive Virtual Reality Proceedings of the International Conference on CSCL 2019 Proceedings Lyon, France 17–21 June 2019 997 1000
30. Perez-Ramirez M. Arroyo-Figueroa G. Ayala A. The use of a virtual reality training system to improve technical skill in the maintenance of live-line power distribution networks J. Interact. Learn. Environ. 2021 29 1 18 10.1080/10494820.2019.1587636
31. Silva A.C. Cardoso A. Lamounier E.A. Barreto C.L. Virtual Reality for Monitor and Control of Electrical Substations An. Acad. Bras. Ciências 2021 93 e20200267 10.1590/0001-3765202120200267
32. Memik N. The Virtual reality electrical substation field trip: Exploring student perceptions and cognitive learning STEM Educ. 2021 1 47 59 10.3934/steme.2021004
33. Xue Y. Wu R. Liu J. Tang X. Crowd Evacuation Guidance Based on Combined Action Reinforcement Learning Algorithms 2021 14 26 10.3390/a14010026
34. Ibrahim Sohail M. Dong W. Yang Q. Machine learning driven smart electric power systems: Current trends and new perspectives Appl. Energy 2020 272 115237 10.1016/j.apenergy.2020.115237
35. Zheng W. He Q. Zhao Y. Machine learning for battery research J. Power Sources 2022 549 232125 10.1016/j.jpowsour.2022.232125
36. Pang Y. Li Y. Feng Z. Feng Z. Zhao Z. Chen S. Zhang H. Forest fire occurrence prediction in China based on machine learning methods Remote Sens. 2022 14 5546 10.3390/rs14215546
37. Mohajane M. Costache R. Karimi F. Pham Q.B. Essahlaoui A. Nguyen H. Laneve G. Oudija F. Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area Ecol. Indic. 2021 129 107869 10.1016/j.ecolind.2021.107869
38. Subramanian S. Crowley M. Using Spatial Reinforcement Learning to Build Forest Wildfire Dynamics Models From Satellite Images Frontiers 2018 5 6 10.3389/FICT.2018.00006
39. Tüfekci P. Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods Int. J. Electr. Power Energy Syst. 2014 60 126 140 10.1016/j.ijepes.2014.02.027
40. Kunxiang D. Zhang Q. Zhang H. Xiao P. Chen J. Optimal Emergency Evacuation Route Planning Model Based on Fire Prediction Data Mathematics 2022 10 3146 10.3390/math10173146
41. Preeti T. Kanakaraddi S. Beelagi A. Malagi S. Sudi A. Forest fire prediction using machine learning techniques Proceedings of the 2021 International Conference on Intelligent Technologies (CONIT) Hubli, India 25–27 June 2021
42. Coffield S.R. Graff C.A. Chen Y. Smyth P. Foufoula-Georgiou E. Randerson J.T. Machine learning to predict final fire size at the time of ignition Int. J. Wildland Fire 2019 28 861 873 10.1071/WF19023
43. Almuhaini S.H. Sultana N. Forecasting Long-Term Electricity Consumption in Saudi Arabia Based on Statistical and Machine Learning Algorithms to Enhance Electric Power Supply Management Energies 2023 16 2035 10.3390/en16042035
44. Torp S. Prediction of Battery Materials Properties with Machine Learning: Developing Algorithms to Discover Electrodes for Li-ion and Mg-Ion Batteries Master’s Thesis University of Oslo Oslo, Norway 2020
45. Flamenbaum R.D. Pompo T. Havenstein C. Thiemsuwan J. Machine Learning in Support of Electric Distribution Asset Failure Prediction, (SMU Data Science Review 2019) Volume 2: No. 2, Article 16 Available online: https://scholar.smu.edu/datasciencereview/vol2/iss2/16 (accessed on 10 May 2025)
46. Goodfellow I. Bengio Y. Courville A. Bengio Y. Deep Learning MIT Press Cambridge, MA, USA 2016 Volume 1 no. 2
47. Byoungjun K. Lee J. A video-based fire detection using deep learning models Appl. Sci. 2019 9 2862 10.3390/app9142862
48. Ozcanli Asiye K. Yaprakdal F. Baysal M. Deep learning methods and applications for electrical power systems: A comprehensive review Int. J. Energy Res. 2020 44 7136 7157 10.1002/er.5331
49. Sun L. Yan J. Qin W. Path planning for multiple agents in an unknown environment using soft actor critic and curriculum learning Comput. Animat. Virtual Worlds 2023 34 e2113 10.1002/cav.2113
50. Viola N. A Reinforcement Learning Approach to Real-Time Emergency Evacuation 2024 Available online: https://www.diva-portal.org/smash/get/diva2:1891315/FULLTEXT01.pdf (accessed on 10 May 2025)
51. Couto G.C.K. Antonelo E.A. Generative adversarial imitation learning for end-to-end autonomous driving on urban environments Proceedings of the 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Orlando, FL, USA 5–7 December 2021
52. Codevilla F. Santana E. López A.M. Gaidon A. Exploring the limitations of behavior cloning for autonomous driving Proceedings of the IEEE/CVF International Conference on Computer Vision Seoul, Republic of Korea 27 October–2 November 2019
53. Omran S. El Houby E.M. Prediction of electrical power disturbances using machine learning techniques J. Ambient. Intell. Humaniz. Comput. 2020 11 2987 3003 10.1007/s12652-019-01440-w
54. Yu N. Machine Learning and Big Data Analytics in Power Distribution Systems, (IEEE BDA Tutorial Series) IEEE Power & Energy Society Available online: https://www.youtube.com/watch?v=4TdHoZYPk68 (accessed on 10 May 2025)
55. Saad M.H. Said A. Machine learning-based fault diagnosis for research nuclear reactor medium voltage power cables in fraction Fourier domain Electr. Eng. 2023 105 25 42 10.1007/s00202-022-01649-7
56. Dampage U. Bandaranayake L. Wanasinghe R. Kottahachchi K. Jayasanka B. Forest fire detection system using wireless sensor networks and machine learning Sci. Rep. 2022 12 46 10.1038/s41598-021-03882-9
57. Sendek A. Cubuk E.D. Antoniuk E. Cheon G. Cui Y. Reed E.J. Machine Learning-Assisted Discovery of Solid Li-Ion Conducting Materials Chem. Mater. 2018 31 10.1021/acs.chemmater.8b03272
58. Sendek A. Stanford ENERGY Available online: https://www.youtube.com/watch?v=TSdgXlwIgtU (accessed on 10 May 2025)

Issue