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

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  • 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
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    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.
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