IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i10p3679-d817851.html
   My bibliography  Save this article

Prediction of Charging Demand of Electric City Buses of Helsinki, Finland by Random Forest

Author

Listed:
  • Sanchari Deb

    (School of Engineering, University of Warwick, Coventry CV4 7AL, UK)

  • Xiao-Zhi Gao

    (School of Computing, University of Eastern Finland, 70211 Kuopio, Finland)

Abstract

Climate change, global warming, pollution, and energy crisis are the major growing concerns of this era, which have initiated the electrification of transport. The electrification of roadway transport has the potential to drastically reduce pollution and the growing demand for energy and to increase the load demand of the power grid, thereby giving a rise to technological and commercial challenges. Thus, charging load prediction is a crucial and demanding issue for maintaining the security and stability of power systems. During recent years, random forest has gained a lot of popularity as a powerful machine learning technique for classification as well as regression analysis. This work develops a random forest (RF)-based approach for predicting charging demand. The proposed method is validated for the prediction of public e-bus charging demand in the city of Helsinki, Finland. The simulation results demonstrate the effectiveness of our scheme.

Suggested Citation

  • Sanchari Deb & Xiao-Zhi Gao, 2022. "Prediction of Charging Demand of Electric City Buses of Helsinki, Finland by Random Forest," Energies, MDPI, vol. 15(10), pages 1-18, May.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3679-:d:817851
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/10/3679/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/10/3679/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yifeng Xue & Xizi Cao & Yi Ai & Kangli Xu & Yichen Zhang, 2020. "Primary Air Pollutants Emissions Variation Characteristics and Future Control Strategies for Transportation Sector in Beijing, China," Sustainability, MDPI, vol. 12(10), pages 1-10, May.
    2. Morsy Nour & José Pablo Chaves-Ávila & Gaber Magdy & Álvaro Sánchez-Miralles, 2020. "Review of Positive and Negative Impacts of Electric Vehicles Charging on Electric Power Systems," Energies, MDPI, vol. 13(18), pages 1-34, September.
    3. Zhang, Zhendong & He, Hongwen & Guo, Jinquan & Han, Ruoyan, 2020. "Velocity prediction and profile optimization based real-time energy management strategy for Plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 280(C).
    4. Ahmad Almaghrebi & Fares Aljuheshi & Mostafa Rafaie & Kevin James & Mahmoud Alahmad, 2020. "Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods," Energies, MDPI, vol. 13(16), pages 1-21, August.
    5. Kristoffer W. Lie & Trym A. Synnevåg & Jacob J. Lamb & Kristian M. Lien, 2021. "The Carbon Footprint of Electrified City Buses: A Case Study in Trondheim, Norway," Energies, MDPI, vol. 14(3), pages 1-21, February.
    6. Xing Zhang, 2018. "Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm," Energies, MDPI, vol. 11(6), pages 1-18, June.
    7. Sanchari Deb & Kari Tammi & Karuna Kalita & Pinakeshwar Mahanta, 2018. "Impact of Electric Vehicle Charging Station Load on Distribution Network," Energies, MDPI, vol. 11(1), pages 1-25, January.
    8. Rahbari, Omid & Omar, Noshin & Firouz, Yousef & Rosen, Marc A. & Goutam, Shovon & Van Den Bossche, Peter & Van Mierlo, Joeri, 2018. "A novel state of charge and capacity estimation technique for electric vehicles connected to a smart grid based on inverse theory and a metaheuristic algorithm," Energy, Elsevier, vol. 155(C), pages 1047-1058.
    9. Krzysztof Zagrajek & Józef Paska & Mariusz Kłos & Karol Pawlak & Piotr Marchel & Magdalena Bartecka & Łukasz Michalski & Paweł Terlikowski, 2020. "Impact of Electric Bus Charging on Distribution Substation and Local Grid in Warsaw," Energies, MDPI, vol. 13(5), pages 1-13, March.
    10. Zhao, Yang & Wang, Zhenpo & Shen, Zuo-Jun Max & Sun, Fengchun, 2021. "Data-driven framework for large-scale prediction of charging energy in electric vehicles," Applied Energy, Elsevier, vol. 282(PB).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hao Yang & Maoyu Ran & Chaoqun Zhuang, 2022. "Prediction of Building Electricity Consumption Based on Joinpoint−Multiple Linear Regression," Energies, MDPI, vol. 15(22), pages 1-19, November.
    2. Pampa Sinha & Kaushik Paul & Sanchari Deb & Sulabh Sachan, 2023. "Comprehensive Review Based on the Impact of Integrating Electric Vehicle and Renewable Energy Sources to the Grid," Energies, MDPI, vol. 16(6), pages 1-39, March.
    3. Praveen Prakash Singh & Fushuan Wen & Ivo Palu & Sulabh Sachan & Sanchari Deb, 2022. "Electric Vehicles Charging Infrastructure Demand and Deployment: Challenges and Solutions," Energies, MDPI, vol. 16(1), pages 1-21, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sanchari Deb, 2021. "Machine Learning for Solving Charging Infrastructure Planning Problems: A Comprehensive Review," Energies, MDPI, vol. 14(23), pages 1-19, November.
    2. Aleksander Chudy & Piotr Hołyszko & Paweł Mazurek, 2022. "Fast Charging of an Electric Bus Fleet and Its Impact on the Power Quality Based on On-Site Measurements," Energies, MDPI, vol. 15(15), pages 1-16, July.
    3. Jaikumar Shanmuganathan & Aruldoss Albert Victoire & Gobu Balraj & Amalraj Victoire, 2022. "Deep Learning LSTM Recurrent Neural Network Model for Prediction of Electric Vehicle Charging Demand," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    4. Zhang, Xiaofeng & Kong, Xiaoying & Yan, Renshi & Liu, Yuting & Xia, Peng & Sun, Xiaoqin & Zeng, Rong & Li, Hongqiang, 2023. "Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior," Energy, Elsevier, vol. 264(C).
    5. Kayhan Alamatsaz & Sadam Hussain & Chunyan Lai & Ursula Eicker, 2022. "Electric Bus Scheduling and Timetabling, Fast Charging Infrastructure Planning, and Their Impact on the Grid: A Review," Energies, MDPI, vol. 15(21), pages 1-39, October.
    6. Cui, Dingsong & Wang, Zhenpo & Liu, Peng & Wang, Shuo & Zhao, Yiwen & Zhan, Weipeng, 2023. "Stacking regression technology with event profile for electric vehicle fast charging behavior prediction," Applied Energy, Elsevier, vol. 336(C).
    7. Manzolli, Jônatas Augusto & Trovão, João Pedro & Antunes, Carlos Henggeler, 2022. "A review of electric bus vehicles research topics – Methods and trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    8. Buzna, Luboš & De Falco, Pasquale & Ferruzzi, Gabriella & Khormali, Shahab & Proto, Daniela & Refa, Nazir & Straka, Milan & van der Poel, Gijs, 2021. "An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations," Applied Energy, Elsevier, vol. 283(C).
    9. Paulo M. De Oliveira-De Jesus & Mario A. Rios & Gustavo A. Ramos, 2018. "Energy Loss Allocation in Smart Distribution Systems with Electric Vehicle Integration," Energies, MDPI, vol. 11(8), pages 1-19, July.
    10. Raeesi, Ramin & Sahebjamnia, Navid & Mansouri, S. Afshin, 2023. "The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 943-973.
    11. Ke Song & Yimin Wang & Cancan An & Hongjie Xu & Yuhang Ding, 2021. "Design and Validation of Energy Management Strategy for Extended-Range Fuel Cell Electric Vehicle Using Bond Graph Method," Energies, MDPI, vol. 14(2), pages 1-31, January.
    12. Batara Surya & Hamsina Hamsina & Ridwan Ridwan & Baharuddin Baharuddin & Firman Menne & Andi Tenri Fitriyah & Emil Salim Rasyidi, 2020. "The Complexity of Space Utilization and Environmental Pollution Control in the Main Corridor of Makassar City, South Sulawesi, Indonesia," Sustainability, MDPI, vol. 12(21), pages 1-41, November.
    13. Lu Wang & Xue Chen & Yan Xia & Linhui Jiang & Jianjie Ye & Tangyan Hou & Liqiang Wang & Yibo Zhang & Mengying Li & Zhen Li & Zhe Song & Yaping Jiang & Weiping Liu & Pengfei Li & Xiaoye Zhang & Shaocai, 2022. "Operational Data-Driven Intelligent Modelling and Visualization System for Real-World, On-Road Vehicle Emissions—A Case Study in Hangzhou City, China," Sustainability, MDPI, vol. 14(9), pages 1-22, April.
    14. Thangaraj Yuvaraj & Thirukoilur Dhandapani Suresh & Arokiasamy Ananthi Christy & Thanikanti Sudhakar Babu & Benedetto Nastasi, 2023. "Modelling and Allocation of Hydrogen-Fuel-Cell-Based Distributed Generation to Mitigate Electric Vehicle Charging Station Impact and Reliability Analysis on Electrical Distribution Systems," Energies, MDPI, vol. 16(19), pages 1-31, September.
    15. K. Habibul Kabir & Shafquat Yasar Aurko & Md. Saifur Rahman, 2021. "Smart Power Management in OIC Countries: A Critical Overview Using SWOT-AHP and Hybrid MCDM Analysis," Energies, MDPI, vol. 14(20), pages 1-50, October.
    16. Despoina Kothona & Aggelos S. Bouhouras, 2022. "A Two-Stage EV Charging Planning and Network Reconfiguration Methodology towards Power Loss Minimization in Low and Medium Voltage Distribution Networks," Energies, MDPI, vol. 15(10), pages 1-17, May.
    17. Adrian Ostermann & Yann Fabel & Kim Ouan & Hyein Koo, 2022. "Forecasting Charging Point Occupancy Using Supervised Learning Algorithms," Energies, MDPI, vol. 15(9), pages 1-23, May.
    18. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    19. Huang, Ruchen & He, Hongwen & Zhao, Xuyang & Wang, Yunlong & Li, Menglin, 2022. "Battery health-aware and naturalistic data-driven energy management for hybrid electric bus based on TD3 deep reinforcement learning algorithm," Applied Energy, Elsevier, vol. 321(C).
    20. Jay Johnson & Timothy Berg & Benjamin Anderson & Brian Wright, 2022. "Review of Electric Vehicle Charger Cybersecurity Vulnerabilities, Potential Impacts, and Defenses," Energies, MDPI, vol. 15(11), pages 1-26, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3679-:d:817851. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.