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A Numerical Analysis Based Internet of Things (IoT) and Big Data Analytics to Minimize Energy Consumption in Smart Buildings

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C. K. Metallidou, K. E. Psannis, E. A. Egyptiadou, “Energy Efficiency in Smart Buildings: IoT Approaches, Journals & Magazines,” IEEE Access, vol. 8, March 11, 2020. doi: 10.1109/ACCESS.2020.2984461. Search in Google Scholar

L. P. C. Zangheri, D. Paci, M. N. E. Labanca, S. T. Ribeiro, S. Panev, P. Zancanella, and J.-S. Broc, “Assessment of Second Long-Term Renovation Strategies Under the Energy Efficiency Directive,” Luxembourg, U.K.: Publications Office of the European Union, 2019. Available: https://ec.europa.eu/jrc/en/publication/assessmentsecond-long-term-renovation-strategies-underenergy-efficiency-directive Search in Google Scholar

A. P. Plageras, K. E. Psannis, C. Stergiou, H. Wang, and B. B. Gupta, “Efficient IoT-based sensor big data collection–processing and analysis in smart buildings,” Future Gener. Comput. Syst., vol. 82, pp. 349–357, May 2018. doi: 10.1016/j.future.2017.09.082. Search in Google Scholar

R. Wegmueller, G. Magnin, J. Robadey, and E.-L. Niederhauser, “Controlled active thermal storage in smart PCM walls for energy independent building applications,” in Proc. 5th Int. Conf. Renew. Energy, Gener. Appl. (ICREGA), Al Ain, United Arab Emirates, Feb. 2018, pp. 154–157, doi: 10.1109/ICREGA.2018.8337575. Search in Google Scholar

N. Khan, N. Pathak, and N. Roy, “Detecting common insulation problems in built environments using thermal images,” in Proc. IEEE Int. Conf. Smart Comput. (SMARTCOMP), Washington, DC, USA, Jun. 2019, pp. 454–458, doi: 10.1109/SMARTCOMP.2019.00087. Search in Google Scholar

M. Sophocleous, P. Savva, M. F. Petrou, J. K. Atkinson, and J. Georgiou, “A durable, screenprinted sensor for in situ and real-time monitoring of concrete’s electrical resistivity suitable for smart buildings/cities and IoT,” IEEE Sensors Lett., vol. 2, no. 4, pp. 1–4, Dec. 2018, doi: 10. 1109/LSENS.2018.2871517. Search in Google Scholar

A. Kumar, A. Kumar, and A. Singh, “Energy efficient and low cost air quality sensor for smart buildings,” in Proc. 3rd Int. Conf. Comput. Intell. Commun. Technol. (CICT), Feb. 2017, pp. 1–4, doi: 10. 1109/CIACT.2017.7977310. Search in Google Scholar

N. Haidar, N. Tamani, F. Nienaber, M. T. Wesseling, A. Bouju, and Y. Ghamri-Doudane, “Data collection period and sensor selection method for smart building occupancy prediction,” in Proc. IEEE 89th Veh. Technol. Conf. (VTC-Spring), Apr. 2019, pp. 1–6, doi: 10. 1109/VTCSpring.2019.8746447. Search in Google Scholar

S. Antonov, “Smart solution for fire safety in a large garage,” in Proc. Int. Conf. Creative Bus. Smart Sustain. Growth (CREBUS), Sandanski, Bulgaria, Mar. 2019, pp. 1–4, doi: 10.1109/CREBUS. 2019.8840089. Search in Google Scholar

G. Cavalera, R. C. Rosito, V. Lacasa, M. Mongiello, F. Nocera, L. Patrono, and I. Sergi, “An innovative smart system based on IoT technologies for fire and danger situations,” in Proc. 4th Int. Conf. Smart Sustain. Technol. (SpliTech), Split, Croatia, Jun. 2019, pp. 1–6, doi: 10.23919/SpliTech.2019.8783059. Search in Google Scholar

P. G. Jeyasheeli, and J. V. J. Selva, “An IOT design for smart lighting in green buildings based on environmental factors,” in Proc. 4th Int. Conf. Adv. Comput. Commun. Syst. (ICACCS), Coimbatore, India, Jan. 2017, pp. 1–5, doi: 10.1109/ICACCS.2017.8014559. Search in Google Scholar

A. Pandharipande, M. Zhao, and E. Frimout, “Connected indoor lighting based applications in a building IoT ecosystem,” IEEE Internet Things Mag., vol. 2, no. 1, Mar. 2019, pp. 22–26, doi: 10.1109/IOTM.2019.1900016. Search in Google Scholar

A. Zakharov, A. Romazanov, A. Shirokikh, and I. Zakharova, “Intellectual data analysis system of building temperature mode monitoring,” in Proc. Int. Russian Autom. Conf. (RusAutoCon), Sochi, Russia, Sep. 2019, pp. 1–6, doi: 10.1109/RUSAUTOCON.2019.8867611. Search in Google Scholar

N. M. Elsayed, R. A. Swief, S. O. Abdellatif, and T. S. Abdel-Salam, “Photovoltaic applications for lighting load energy saving: Case studies, educational building,” in Proc. Int. Conf. Innov. Trends Comput. Eng. (ITCE), Aswan, Egypt, Feb. 2019, pp. 564–569, doi: 10.1109/ITCE.2019.8646485. Search in Google Scholar

P. D. Leo, F. Spertino, S. Fichera, G. Malgaroli, and A. Ratclif, “Improvement of self-sufficiency for an innovative nearly zero energy building by photovoltaic generators,” in Proc. IEEE Milan PowerTech, Milan, Italy, Jun. 2019, pp. 1–6, doi: 10.1109/PTC.2019.8810434. Search in Google Scholar

K. R. Babu, and C. Vyjayanthi, “Implementation of net zero energy building (NZEB) prototype with renewable energy integration,” in Proc. IEEE Region 10 Symp. (TENSYMP), Kochi, India, Jul. 2017, pp. 1–5, doi: 10.1109/TENCONSpring. 2017.8069994. Search in Google Scholar

I. Ilhan, M. Karakose, and M. Yavas, “Design and simulation of intelligent central heating system for smart buildings in smart city,” in Proc. 7th Int. Istanbul Smart Grids Cities Congr. Fair (ICSG), Istanbul, Turkey, Apr. 2019, pp. 233–237, doi: 10.1109/SGCF.2019.8782356. Search in Google Scholar

T. Sonnekalb and S. Lucia, “Smart hot water control with learned human behavior for minimal energy consumption,” in Proc. IEEE 5th World Forum Internet Things (WF-IoT), Limerick, Republic of Ireland, Apr. 2019, pp. 572–577, doi: 10.1109/WF-IoT.2019.8767171. Search in Google Scholar

D. Alulema, M. Zapata, and M. A. Zapata, “An IoT-based remote monitoring system for electrical power consumption via Web-application,” in Proc. Int. Conf. Inf. Syst. Comput. Sci. (INCISCOS), Quito, Ecuador, Nov. 2018, pp. 193–197, doi: 10.1109/INCISCOS.2018.00035. Search in Google Scholar

A. M. Ali, S. A. A. Shukor, N. A. Rahim, Z. M. Razlan, Z. A. Z. Jamal, and K. Kohlhof, “IoT-based smart air conditioning control for thermal comfort,” in Proc. IEEE Int. Conf. Autom. Control Intell. Syst. (I2CACIS), Selangor, Malaysia, Jun. 2019, pp. 289–294, doi: 10.1109/I2CACIS.2019.8825079. Search in Google Scholar

G. Alsuhli and A. Khattab, “A fog-based IoT platform for smart buildings,” in Proc. Int. Conf. Innov. Trends Comput. Eng. (ITCE), Aswan, Egypt, Feb. 2019, pp. 174–179, doi: 10.1109/ITCE.2019.8646480. Search in Google Scholar

X. Zhang, M. Pipattanasomporn, T. Chen, and S. Rahman, “An IoT-based thermal model learning framework for smart buildings,” IEEE Internet Things J., vol. 7, no. 1, Jan. 2020, pp. 518–527, doi: 10.1109/JIOT.2019.2951106. Search in Google Scholar

J. Aguilar, A. Garces-Jimenez, N. Gallego-Salvador, J. A. G. De Mesa, J. M. Gomez-Pulido, and A. J. Garcia-Tejedor, “Autonomic management architecture for multi-HVAC systems in smart buildings,” IEEE Access, vol. 7, 2019, pp. 123402–123415, doi: 10.1109/ACCESS.2019.2937639. Search in Google Scholar

S. Rastegarpour, M. Ghaemi, and L. Ferrarini, “A predictive control strategy for energy management in buildings with radiant floors and thermal storage,” in Proc. SICE Int. Symp. Control Syst. (SICE ISCS), Tokyo, Japan, Mar. 2018, pp. 67–73, doi: 10.23919/SICEISCS.2018.8330158. Search in Google Scholar

A. Gillespie, T. F. Xulu, S. I. Noubissie Tientcheu, and S. D. Chowdhury, “Building design considerations for an energy efficient HVAC system,” in Proc. IEEE PES/IAS PowerAfrica, Cape Town, South Africa, Jun. 2018, pp. 1–6, doi: 10.1109/PowerAfrica.2018.8520995. Search in Google Scholar

B. Seng, C. Magniont, S. Spagnol, and S. Lorente, “Evaluation of hemp concrete thermal properties,” in Proc. Int. IEEE Conf. Ubiquitous Intell. Comput., Adv. Trusted Comput., Scalable Comput. Commun., Cloud big data Comput., Internet People, Smart World Congr. (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), Toulouse, France, Jul. 2016, pp. 984–989, doi: 10.1109/UIC-ATC-ScalComCBDCom-IoPSmartWorld. 2016.0154. Search in Google Scholar

El Mallahi, I., Riffi, J., Tairi, H., Ez-Zahout, A., and Mahraz, M. A. . (2023). A Distributed Big Data Analytics Models for Traffic Accidents Classification and Recognition based SparkMlLib Cores. Journal of Automation, Mobile Robotics and Intelligent Systems, 16(4), 62-71. doi: 10.14313/JAMRIS/4-2022/34. Search in Google Scholar

Rahman Shafique, Furqan Rustam, Sheriff Murtala, Anca Delia Jurcut, Gyu Sang Choi, “Advancing Autonomous Vehicle Safety: Machine Learning to Predict Sensor-Related Accident Severity”, IEEE Access, vol. 12, pp. 25933–25948, 2024. Search in Google Scholar

Nassim Sohaee, Shahram Bohluli, “Nonlinear Analysis of the Effects of Socioeconomic, Demographic, and Technological Factors on the Number of Fatal Traffic Accidents”, Safety, vol. 10, no. 1, pp. 11, 2024 Search in Google Scholar

I. E. Mallahi, A. Dlia, J. Riffi, M. A. Mahraz and H. Tairi, “Prediction of Traffic Accidents using Random Forest Model,” 2022 International Conference on Intelligent Systems and Computer Vision (ISCV), Fez, Morocco, 2022, pp. 1–7, doi: 10.1109/ISCV54655.2022.9806099. Search in Google Scholar

Nasry, A., Ezzahout, A., and Omary, F. . (2023). People Tracking in Video Surveillance Systems Based on Artificial Intelligence. Journal of Automation, Mobile Robotics and Intelligent Systems, 17(1), 59–68. doi: 10.14313/JAMRIS/1-2023/8. Search in Google Scholar

Ndayikengurukiye, A., Ez-zahout, A., Aboubakr, A., Charkaoui, Y., and Fouzia, O. (2022). Resource Optimisation in Cloud Computing: Comparative Study of Algorithms Applied to Recommendations in a Big Data Analysis Architecture. Journal of Automation, Mobile Robotics and Intelligent Systems, 15(4), 65–75. doi: 10.14313/JAMRIS/4-2021/28. Search in Google Scholar