نبذة مختصرة : In this document, the development of firmware and hardware algorithms was performed, as well as an artificial intelligence application to learn the user’s standard regarding the desired lightlevel.TheaboveimplementationshadastheirguidingelementtheSmartLVGridframework which consists of a meta model that converges passive low voltage circuits into a Smart Grid. The systematic model was used for the convergence to the Smart Building paradigm. Concepts inherent to the framework were used as systematic communication between elements that make up the system as well as the retrofit. The adaptation of the framework for convergence Smart Building will be made at the Embedded Systems Laboratory located at HUB innovation and technology,which,inturn,islocatedattheSchoolofTechnology.Thefirmwaregoesthroughthe implementation of the network connection using MQTT protocol indicated for such applications, besides favoring the acquisition of environment data through the implementation of sensing platform. Finally, the artificial intelligence model will gather the available information from the environmentandtheusertolearnandmakethebrightnessregulationintelligentandautonomous. After the implementation and collection of the result, the goal is to achieve convergence Smart Building through SmartLVGrid with development of artificial intelligence. ; Neste trabalho, foi realizado o desenvolvimento de algoritmos de firmware e hardware além de uma aplicação de inteligência artificial para aprendizado do padrão do usuário no que diz respeito a nível de luminosidade desejado. As implementações acima tinham como elemento norteadoroframeworkSmartLVGridqueconsisteemummetamodeloquerealizaaconvergência dos circuitos de baixa tensão passivos em uma Smart Grid. O modelo sistemático foi utilizado para a convergência ao paradigma Smart Building. Conceitos inerentes ao framework foram utilizados como comunicação sistemática entre elementos que compõe o sistema bem como o retrofit. A adaptação do framework para convergência Smart Building será feita no ...
Relation: A, J. et al. Intelligent smart home automation and security system using arduino and wi-fi. International Journal Of Engineering And Computer Science, Valley International, mar 2017. ADAFRUIT. 2019. . Acessado em 28/10/2019. AL-FUQAHA, A. et al. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, Institute of Electrical and Electronics Engineers (IEEE), v. 17, n. 4, p. 2347–2376, 2015. ARTIGO. 2019. . Acessado em 25/10/2019. BAHGA, A.; MADISETTI, V. Internet of Things: A hands-on approach. [S.l.]: Vpt, 2014. BRAGA, A. de P.; FERREIRA, A. C. P. de L.; LUDERMIR, T. B. Redes neurais artificiais: teoria e aplicações. [S.l.]: LTC Editora Rio de Janeiro, Brazil:, 2007. BRINK, H. et al. Real-world machine learning. [S.l.]: Manning, 2017. CLEMENTS-CROOME, D. Sustainable intelligent buildings for people: a review. Intelligent Buildings International, Taylor & Francis, v. 3, n. 2, p. 67–86, 2011. CLEMENTS-CROOME, D.; CROOME, D. J. Intelligent buildings: design, management and operation. [S.l.]: Thomas Telford, 2004. CREME. 2019. . Acessado em 28/10/2019. DONG, L. et al. The impact of led correlated color temperature on visual performance under mesopic conditions. IEEE Photonics Journal, IEEE, v. 9, n. 6, p. 1–16, 2017. EASTMAN, C. et al. BIM handbook: A guide to building information modeling for owners, managers, designers, engineers and contractors. [S.l.]: John Wiley & Sons, 2011. ECP. 2019. . Acessado em 28/10/2019. ELéTRICA. 2019. . Acessado em 28/10/2019. ESPRESSIF. Datasheet ESP32-Wroom 32D. 2019. FACELI, K. et al. Artificial intelligence: a machine learning approach. LTC, Rio de Janeiro, 2011. FERNANDES, R.; GUIMARÃES, W. Implementation of a buck converter with hysteresis voltage control applied to led chip array package for street lighting. In: IEEE. 2018 Argentine Conference on Automatic Control (AADECA). [S.l.], 2018. p. 1–6. FERREIRA, A. R.; TOMIOKA, J. Iluminação de estado sólido, economia potencial de energia elétrica para o país. In: Anais do VIII Workshop de Pós-Graduação e Pesquisa do Centro Paula Souza, São Paulo, São Paulo, Brasil. [S.l.: s.n.], 2013. FLACH, P. Machine learning: the art and science of algorithms that make sense of data. [S.l.]: Cambridge University Press, 2012. 81 FÜRNKRANZ, J.; GAMBERGER, D.; LAVRAˇC, N. Foundations of rule learning. [S.l.]: Springer Science & Business Media, 2012. GÉRON, A. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. [S.l.]: O’Reilly Media, 2019. GOMES, R. C. S. et al. Automation meta-system applied to smart grid convergence of low voltage distribution legacy grids. In: IEEE. 2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE). [S.l.], 2017. p. 400–413. HANES, D. et al. IoT fundamentals: Networking technologies, protocols, and use cases for the internet of things. [S.l.]: Cisco Press, 2017. HAYKIN, S. S. et al. Neural networks and learning machines. [S.l.]: Pearson education Upper Saddle River, 2009. v. 3. ILUMININ. 2018. . Acessado em 25/10/2019. INTELLIPAAT. 2019. . Acessado em 24/11/2019. KANSARA, V. K. Solar & led technology for energy efficient substation. In: IEEE. 2017 IEEE Region 10 Symposium (TENSYMP). [S.l.], 2017. p. 1–5. KAZAK, A. N.; BUCHATSKIY, P. Perspectives for smart city technologies in the resort region. In: 2018 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies"(IT&QM&IS). [S.l.]: IEEE, 2018. KUBAT, M. An introduction to machine learning. [S.l.]: Springer, 2017. v. 2. LOSING, V.; HAMMER, B.; WERSING, H. Incremental on-line learning: A review and comparison of state of the art algorithms. Neurocomputing, Elsevier, v. 275, p. 1261–1274, 2018. MAKERS. 2019. . Acessado em 30/10/2019. MARSLAND, S. Machine learning: an algorithmic perspective. [S.l.]: Chapman and Hall/CRC, 2011. MONTEIRO, R. V. A. et al. Led tubular lamps and tubular fluorescent: Power quality. In: IEEE. 2014 16th International Conference on Harmonics and Quality of Power (ICHQP). [S.l.], 2014. p. 400–404. MQTT. 2019. . Acessado em 28/10/2019. NGUYEN, T. A.; AIELLO, M. Energy intelligent buildings based on user activity: A survey. Energy and buildings, Elsevier, v. 56, p. 244–257, 2013. NUMPY. 2017. . Acessado em 25/10/2019. PAHO. 2019. . Acessado em 28/10/2019. 82 PANDAS. 2019. . Acessado em 25/10/2019. PEDREGOSA, F. et al. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, v. 12, p. 2825–2830, 2011. PEREIRA, A. et al. Some considerations about led technology in public lighting. In: IEEE. 2015 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON). [S.l.], 2015. p. 561–565. PERERA, C. et al. A survey on internet of things from industrial market perspective. IEEE Access, Institute of Electrical and Electronics Engineers (IEEE), v. 2, p. 1660–1679, 2014. RANGEL, M. G.; SILVA, P. B.; GUEDE, J. R. A. Led–iluminação de estado sólido. São José do Campos, 2009. ROJAS, R. Neural networks: a systematic introduction. [S.l.]: Springer Science & Business Media, 2013. SMARTBUILDING. 2018. . Acessado em 22/04/2019.; http://repositorioinstitucional.uea.edu.br//handle/riuea/2305
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