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Reliable Integration of Neural Network and Internet of Things for Forecasting, Controlling, and Monitoring of Experimental Building Management System

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  • Mohamed El-Sayed M. Essa

    (Electrical Power and Machines Department, Institute of Aviation Engineering and Technology (I.A.E.T), Egyptian Aviation Academy, Imbaba Airport, Giza 12815, Egypt)

  • Ahmed M. El-shafeey

    (Electronics and Communication Department, Institute of Aviation Engineering and Technology (I.A.E.T), Egyptian Aviation Academy, Imbaba Airport, Giza 12815, Egypt)

  • Amna Hassan Omar

    (Architecture Engineering Department, Institute of Aviation Engineering and Technology (I.A.E.T), Egyptian Aviation Academy, Imbaba Airport, Giza 12815, Egypt)

  • Adel Essa Fathi

    (Electrical Power and Machines Department, Institute of Aviation Engineering and Technology (I.A.E.T), Egyptian Aviation Academy, Imbaba Airport, Giza 12815, Egypt)

  • Ahmed Sabry Abo El Maref

    (Electrical Power and Machines Department, Institute of Aviation Engineering and Technology (I.A.E.T), Egyptian Aviation Academy, Imbaba Airport, Giza 12815, Egypt)

  • Joseph Victor W. Lotfy

    (Electrical Power and Machines Department, Institute of Aviation Engineering and Technology (I.A.E.T), Egyptian Aviation Academy, Imbaba Airport, Giza 12815, Egypt)

  • Mohamed Saleh El-Sayed

    (Aeronautical Engineering Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt)

Abstract

In this paper, Internet of Things (IoT) and artificial intelligence (AI) are employed to solve the issue of energy consumption in a case study of an education laboratory. IoT enables deployment of AI approaches to establish smart systems and manage the sensor signals between different equipment based on smart decisions. As a result, this paper introduces the design and investigation of an experimental building management system (BMS)-based IoT approach to monitor status of sensors and control operation of loads to reduce energy consumption. The proposed BMS is built on integration between a programmable logic controller (PLC), a Node MCU ESP8266, and an Arduino Mega 2560 to perform the roles of transferring and processing data as well as decision-making. The system employs a variety of sensors, including a DHT11 sensor, an IR sensor, a smoke sensor, and an ultrasonic sensor. The collected IoT data from temperature sensors are used to build an artificial neural network (ANN) model to forecast the temperature inside the laboratory. The proposed IoT platform is created by the ThingSpeak platform, the Bylink dashboard, and a mobile application. The experimental results show that the experimental BMS can monitor the sensor data and publish the data on different IoT platforms. In addition, the results demonstrate that operation of the air-conditioning, lighting, firefighting, and ventilation systems could be optimally monitored and managed for a smart system with an architectural design. Furthermore, the results prove that the ANN model can perform a distinct temperature forecasting process based on IoT data.

Suggested Citation

  • Mohamed El-Sayed M. Essa & Ahmed M. El-shafeey & Amna Hassan Omar & Adel Essa Fathi & Ahmed Sabry Abo El Maref & Joseph Victor W. Lotfy & Mohamed Saleh El-Sayed, 2023. "Reliable Integration of Neural Network and Internet of Things for Forecasting, Controlling, and Monitoring of Experimental Building Management System," Sustainability, MDPI, vol. 15(3), pages 1-29, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2168-:d:1045466
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    References listed on IDEAS

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    1. Chiara Bersani & Carmelina Ruggiero & Roberto Sacile & Abdellatif Soussi & Enrico Zero, 2022. "Internet of Things Approaches for Monitoring and Control of Smart Greenhouses in Industry 4.0," Energies, MDPI, vol. 15(10), pages 1-30, May.
    2. Lee, In & Lee, Kyoochun, 2015. "The Internet of Things (IoT): Applications, investments, and challenges for enterprises," Business Horizons, Elsevier, vol. 58(4), pages 431-440.
    3. Bruno Mataloto & Daniel Calé & Kaiser Carimo & Joao C. Ferreira & Ricardo Resende, 2021. "3D IoT System for Environmental and Energy Consumption Monitoring System," Sustainability, MDPI, vol. 13(3), pages 1-19, February.
    4. Carmen De la Cruz-Lovera & Alberto-Jesús Perea-Moreno & José-Luis De la Cruz-Fernández & José Antonio Alvarez-Bermejo & Francisco Manzano-Agugliaro, 2017. "Worldwide Research on Energy Efficiency and Sustainability in Public Buildings," Sustainability, MDPI, vol. 9(8), pages 1-20, July.
    5. Di Foggia, Giacomo, 2018. "Energy efficiency measures in buildings for achieving sustainable development goals," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 4(11).
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    Cited by:

    1. Dalia Mohammed Talat Ebrahim Ali & Violeta Motuzienė & Rasa Džiugaitė-Tumėnienė, 2024. "AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings," Energies, MDPI, vol. 17(17), pages 1-35, August.

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