IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i11p3202-d565684.html
   My bibliography  Save this article

Assessment of Energy Use Based on an Implementation of IoT, Cloud Systems, and Artificial Intelligence

Author

Listed:
  • Ciprian Mihai Coman

    (ITC Department, Tesagon International SRL, Ploiesti 100029, Romania
    Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, Bucharest 060042, Romania)

  • Adriana Florescu

    (Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, Bucharest 060042, Romania)

  • Constantin Daniel Oancea

    (Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, Bucharest 060042, Romania)

Abstract

Nowadays products are developed at a rapid pace, with shorter and shorter times between concept and go to market. With the advancement in technology, product designers and manufacturers can use new approaches to obtain information about their products and transform it into knowledge that they can use to improve the product. We developed the Poket Framework platform to facilitate the generation of product knowledge. In order to increase the reliability and safety in operation of electrical equipment, an evaluation is proposed, through tests and studies, using the original Poket Framework platform. Thus, several tests and studies were performed, which included testing and analyzing the correct integration in several use cases and remote data acquisition, and testing and analysis of the Poket Framework using literature established data sets of household appliances and electrical systems. Possible evolutions and Poket platform extensions are also considered.

Suggested Citation

  • Ciprian Mihai Coman & Adriana Florescu & Constantin Daniel Oancea, 2021. "Assessment of Energy Use Based on an Implementation of IoT, Cloud Systems, and Artificial Intelligence," Energies, MDPI, vol. 14(11), pages 1-21, May.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3202-:d:565684
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/11/3202/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/11/3202/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Aitor Ardanza & Aitor Moreno & Álvaro Segura & Mikel de la Cruz & Daniel Aguinaga, 2019. "Sustainable and flexible industrial human machine interfaces to support adaptable applications in the Industry 4.0 paradigm," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 4045-4059, June.
    2. Ciprian Mihai Coman & Adriana Florescu & Constantin Daniel Oancea, 2020. "Improving the Efficiency and Sustainability of Power Systems Using Distributed Power Factor Correction Methods," Sustainability, MDPI, vol. 12(8), pages 1-20, April.
    3. Hail Jung & Jinsu Jeon & Dahui Choi & Jung-Ywn Park, 2021. "Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry," Sustainability, MDPI, vol. 13(8), pages 1-16, April.
    4. Robert Stanisławski & Andrzej Szymonik, 2021. "Impact of Selected Intelligent Systems in Logistics on the Creation of a Sustainable Market Position of Manufacturing Companies in Poland in the Context of Industry 4.0," Sustainability, MDPI, vol. 13(7), pages 1-25, April.
    5. Bogdan Cristian Florea & Dragos Daniel Taralunga, 2020. "Blockchain IoT for Smart Electric Vehicles Battery Management," Sustainability, MDPI, vol. 12(10), pages 1-25, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Govindan, Kannan & Kannan, Devika & Jørgensen, Thomas Ballegård & Nielsen, Tim Straarup, 2022. "Supply Chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    2. Özden Tozanlı & Elif Kongar & Surendra M. Gupta, 2020. "Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain," Sustainability, MDPI, vol. 12(13), pages 1-33, July.
    3. Shabana Urooj & Fadwa Alrowais & Yuvaraja Teekaraman & Hariprasath Manoharan & Ramya Kuppusamy, 2021. "IoT Based Electric Vehicle Application Using Boosting Algorithm for Smart Cities," Energies, MDPI, vol. 14(4), pages 1-16, February.
    4. Hao Wang & Chen Peng & Bolin Liao & Xinwei Cao & Shuai Li, 2023. "Wind Power Forecasting Based on WaveNet and Multitask Learning," Sustainability, MDPI, vol. 15(14), pages 1-22, July.
    5. Kendrik Yan Hong Lim & Pai Zheng & Chun-Hsien Chen, 2020. "A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1313-1337, August.
    6. Szymon Cyfert & Waldemar Glabiszewski & Maciej Zastempowski, 2021. "Impact of Management Tools Supporting Industry 4.0 on the Importance of CSR during COVID-19. Generation Z," Energies, MDPI, vol. 14(6), pages 1-13, March.
    7. Justyna Patalas-Maliszewska & Hanna Łosyk & Matthias Rehm, 2022. "Decision-Tree Based Methodology Aid in Assessing the Sustainable Development of a Manufacturing Company," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    8. Alisha Lakra & Shubhkirti Gupta & Ravi Ranjan & Sushanta Tripathy & Deepak Singhal, 2022. "The Significance of Machine Learning in the Manufacturing Sector: An ISM Approach," Logistics, MDPI, vol. 6(4), pages 1-15, October.
    9. Dai, Haifeng & Jiang, Bo & Hu, Xiaosong & Lin, Xianke & Wei, Xuezhe & Pecht, Michael, 2021. "Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    10. Marco Bettiol & Mauro Capestro & Eleonora Di Maria & Roberto Ganau, 2024. "Is this time different? How Industry 4.0 affects firms’ labor productivity," Small Business Economics, Springer, vol. 62(4), pages 1449-1467, April.
    11. Valentina De Simone & Valentina Di Pasquale & Maria Elena Nenni & Salvatore Miranda, 2023. "Sustainable Production Planning and Control in Manufacturing Contexts: A Bibliometric Review," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    12. José Salvador da Motta Reis & Maximilian Espuny & Thaís Vieira Nunhes & Nilo Antonio de Souza Sampaio & Raine Isaksson & Fernando Celso de Campos & Otávio José de Oliveira, 2021. "Striding towards Sustainability: A Framework to Overcome Challenges and Explore Opportunities through Industry 4.0," Sustainability, MDPI, vol. 13(9), pages 1-28, May.
    13. Héctor Cañas & Josefa Mula & Francisco Campuzano-Bolarín, 2020. "A General Outline of a Sustainable Supply Chain 4.0," Sustainability, MDPI, vol. 12(19), pages 1-17, September.
    14. Walied Alharbi, 2023. "Assessment of Distribution System Margins Considering Battery Swapping Stations," Sustainability, MDPI, vol. 15(8), pages 1-13, April.
    15. Nguyen, Tiep & Duong, Quang Huy & Nguyen, Truong Van & Zhu, You & Zhou, Li, 2022. "Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 244(C).
    16. Bilal Naji Alhasnawi & Basil H. Jasim & Maria Dolores Esteban & Josep M. Guerrero, 2020. "A Novel Smart Energy Management as a Service over a Cloud Computing Platform for Nanogrid Appliances," Sustainability, MDPI, vol. 12(22), pages 1-47, November.
    17. Bag, Surajit & Pretorius, Jan Ham Christiaan & Gupta, Shivam & Dwivedi, Yogesh K., 2021. "Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    18. Sony, Michael & Antony, Jiju & Mc Dermott, Olivia & Garza-Reyes, Jose Arturo, 2021. "An empirical examination of benefits, challenges, and critical success factors of industry 4.0 in manufacturing and service sector," Technology in Society, Elsevier, vol. 67(C).
    19. Tortorella, Guilherme Luz & Saurin, Tarcisio A. & Hines, Peter & Antony, Jiju & Samson, Daniel, 2023. "Myths and facts of industry 4.0," International Journal of Production Economics, Elsevier, vol. 255(C).
    20. Hail Jung & Jeongjin Rhee, 2022. "Application of YOLO and ResNet in Heat Staking Process Inspection," Sustainability, MDPI, vol. 14(23), pages 1-14, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3202-:d:565684. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.