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Machine Learning Based Vehicle to Grid Strategy for Improving the Energy Performance of Public Buildings

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  • Connor Scott

    (Smart Infrastructure and Industry Research Group, Department of Engineering, Manchester Metropolitan University, Chester St., Manchester M1 5GD, UK)

  • Mominul Ahsan

    (Smart Infrastructure and Industry Research Group, Department of Engineering, Manchester Metropolitan University, Chester St., Manchester M1 5GD, UK)

  • Alhussein Albarbar

    (Smart Infrastructure and Industry Research Group, Department of Engineering, Manchester Metropolitan University, Chester St., Manchester M1 5GD, UK)

Abstract

Carbon neutral buildings are dependent on effective energy management systems and harvesting energy from unpredictable renewable sources. One strategy is to utilise the capacity from electric vehicles, while renewables are not available according to demand. Vehicle to grid (V2G) technology can only be expanded if there is funding and realisation that it works, so investment must be in place first, with charging stations and with the electric vehicles to begin with. The installer of the charging stations will achieve the financial benefit or have an incentive and vice versa for the owners of the electric vehicles. The paper presents an effective V2G strategy that was developed and implemented for an operational university campus. A machine learning algorithm has also been derived to predict energy consumption and energy costs for the investigated building. The accuracy of the developed algorithm in predicting energy consumption was found to be between 94% and 96%, with an average of less than 5% error in costs predictions. The achieved results show that energy consumption savings are in the range of 35%, with the potentials to achieve about 65% if the strategy was applied at all times. This has demonstrated the effectiveness of the machine learning algorithm in carbon print reductions.

Suggested Citation

  • Connor Scott & Mominul Ahsan & Alhussein Albarbar, 2021. "Machine Learning Based Vehicle to Grid Strategy for Improving the Energy Performance of Public Buildings," Sustainability, MDPI, vol. 13(7), pages 1-22, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:7:p:4003-:d:529830
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    References listed on IDEAS

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    Cited by:

    1. Simon P. Philbin, 2021. "Driving Sustainability through Engineering Management and Systems Engineering," Sustainability, MDPI, vol. 13(12), pages 1-7, June.
    2. Vinay Simha Reddy Tappeta & Bhargav Appasani & Suprava Patnaik & Taha Selim Ustun, 2022. "A Review on Emerging Communication and Computational Technologies for Increased Use of Plug-In Electric Vehicles," Energies, MDPI, vol. 15(18), pages 1-26, September.
    3. Qin Chen & Komla Agbenyo Folly, 2022. "Application of Artificial Intelligence for EV Charging and Discharging Scheduling and Dynamic Pricing: A Review," Energies, MDPI, vol. 16(1), pages 1-26, December.
    4. Nnaemeka Vincent Emodi & Scott Dwyer & Kriti Nagrath & John Alabi, 2022. "Electromobility in Australia: Tariff Design Structure and Consumer Preferences for Mobile Distributed Energy Storage," Sustainability, MDPI, vol. 14(11), pages 1-18, May.
    5. Md. Rayid Hasan Mojumder & Fahmida Ahmed Antara & Md. Hasanuzzaman & Basem Alamri & Mohammad Alsharef, 2022. "Electric Vehicle-to-Grid (V2G) Technologies: Impact on the Power Grid and Battery," Sustainability, MDPI, vol. 14(21), pages 1-53, October.
    6. Monica Alonso & Hortensia Amaris & David Martin & Arturo de la Escalera, 2023. "Proximal Policy Optimization for Energy Management of Electric Vehicles and PV Storage Units," Energies, MDPI, vol. 16(15), pages 1-20, July.

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