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Integrating Smart Energy Management System with Internet of Things and Cloud Computing for Efficient Demand Side Management in Smart Grids

Author

Listed:
  • M. Usman Saleem

    (Department of Electrical Engineering, Superior University, Lahore 54000, Pakistan
    Department of Computer Science & Information Technology, Government College Women University, Sialkot 51310, Pakistan)

  • Mustafa Shakir

    (Department of Electrical Engineering, Superior University, Lahore 54000, Pakistan)

  • M. Rehan Usman

    (Department of Electrical Engineering, Superior University, Lahore 54000, Pakistan)

  • M. Hamza Tahir Bajwa

    (Gujranwala Electric Power Company (GEPCO), Gujranwala 52250, Pakistan)

  • Noman Shabbir

    (FinEST Center for Smart Cities, 12616 Tallinn, Estonia)

  • Payam Shams Ghahfarokhi

    (Department of Electrical Power Engineering & Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Kamran Daniel

    (Department of Electrical Power Engineering & Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

Abstract

The increasing price of and demand for energy have prompted several organizations to develop intelligent strategies for energy tracking, control, and conservation. Demand side management is a critical strategy for averting substantial supply disruptions and improving energy efficiency. A vital part of demand side management is a smart energy management system that can aid in cutting expenditures while still satisfying energy needs; produce customers’ energy consumption patterns; and react to energy-saving algorithms and directives. The Internet of Things is an emerging technology that can be employed to effectively manage energy usage in industrial, commercial, and residential sectors in the smart environment. This paper presents a smart energy management system for smart environments that integrates the Energy Controller and IoT middleware module for efficient demand side management. Each device is connected to an energy controller, which is the inculcation of numerous sensors and actuators with an IoT object, collects the data of energy consumption from each smart device through various time-slots that are designed to optimize the energy consumption of air conditioning systems based on ambient temperature conditions and operational dynamics of buildings and then communicate it to a centralized middleware module (cloud server) for management, processing, and further analysis. Since air conditioning systems contribute more than 50% of the electricity consumption in Pakistan, for validation of the proposed system, the air conditioning units have been taken as a proof of concept. The presented approach offers several advantages over traditional controllers by leveraging real-time monitoring, advanced algorithms, and user-friendly interfaces. The evaluation process involves comparing electricity consumption before and after the installation of the SEMS. The proposed system is tested and implemented in four buildings. The results demonstrate significant energy savings ranging from 15% to 49% and highlight the significant benefits of the system. The smart energy management system offers real-time monitoring, better control over the air conditioning systems, cost savings, environmental benefits, and longer equipment life. The ultimate goal is to provide a practical solution for reducing energy consumption in buildings, which can contribute to sustainable and efficient use of energy resources and goes beyond simpler controllers to address the specific needs of energy management in buildings.

Suggested Citation

  • M. Usman Saleem & Mustafa Shakir & M. Rehan Usman & M. Hamza Tahir Bajwa & Noman Shabbir & Payam Shams Ghahfarokhi & Kamran Daniel, 2023. "Integrating Smart Energy Management System with Internet of Things and Cloud Computing for Efficient Demand Side Management in Smart Grids," Energies, MDPI, vol. 16(12), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4835-:d:1175558
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    References listed on IDEAS

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    1. Noman Shabbir & Lauri Kütt & Kamran Daniel & Victor Astapov & Hadi Ashraf Raja & Muhammad Naveed Iqbal & Oleksandr Husev, 2022. "Feasibility Investigation for Residential Battery Sizing Considering EV Charging Demand," Sustainability, MDPI, vol. 14(3), pages 1-13, January.
    2. Rafique, M. Mujahid & Rehman, S., 2017. "National energy scenario of Pakistan – Current status, future alternatives, and institutional infrastructure: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 156-167.
    3. Naser Hossein Motlagh & Mahsa Mohammadrezaei & Julian Hunt & Behnam Zakeri, 2020. "Internet of Things (IoT) and the Energy Sector," Energies, MDPI, vol. 13(2), pages 1-27, January.
    4. Gonçalo Marques & Cristina Roque Ferreira & Rui Pitarma, 2018. "A System Based on the Internet of Things for Real-Time Particle Monitoring in Buildings," IJERPH, MDPI, vol. 15(4), pages 1-14, April.
    5. Younes Sahri & Youcef Belkhier & Salah Tamalouzt & Nasim Ullah & Rabindra Nath Shaw & Md. Shahariar Chowdhury & Kuaanan Techato, 2021. "Energy Management System for Hybrid PV/Wind/Battery/Fuel Cell in Microgrid-Based Hydrogen and Economical Hybrid Battery/Super Capacitor Energy Storage," Energies, MDPI, vol. 14(18), pages 1-32, September.
    6. Tuballa, Maria Lorena & Abundo, Michael Lochinvar, 2016. "A review of the development of Smart Grid technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 710-725.
    7. Du, Yan & Zandi, Helia & Kotevska, Olivera & Kurte, Kuldeep & Munk, Jeffery & Amasyali, Kadir & Mckee, Evan & Li, Fangxing, 2021. "Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning," Applied Energy, Elsevier, vol. 281(C).
    8. Diogo Santos & João C. Ferreira, 2019. "IoT Power Monitoring System for Smart Environments," Sustainability, MDPI, vol. 11(19), pages 1-24, September.
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    Cited by:

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    3. Saini, Krishna Kumar & Sharma, Pavitra & Mathur, Hitesh Datt & Gautam, Aditya R. & Bansal, Ramesh C., 2024. "Techno-economic and reliability assessment of an off-grid solar-powered energy system," Applied Energy, Elsevier, vol. 371(C).
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    5. Samuel Moveh & Emmanuel Alejandro Merchán-Cruz & Maher Abuhussain & Yakubu Aminu Dodo & Saleh Alhumaid & Ali Hussain Alhamami, 2025. "Deep Learning Framework Using Transformer Networks for Multi Building Energy Consumption Prediction in Smart Cities," Energies, MDPI, vol. 18(6), pages 1-22, March.

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