IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v163y2016icp134-141.html
   My bibliography  Save this article

Forecasting the EV charging load based on customer profile or station measurement?

Author

Listed:
  • Majidpour, Mostafa
  • Qiu, Charlie
  • Chu, Peter
  • Pota, Hemanshu R.
  • Gadh, Rajit

Abstract

In this paper, forecasting of the Electric Vehicle (EV) charging load has been based on two different datasets: data from the customer profile (referred to as charging record) and data from outlet measurements (referred to as station record). Four different prediction algorithms namely Time Weighted Dot Product based Nearest Neighbor (TWDP-NN), Modified Pattern Sequence Forecasting (MPSF), Support Vector Regression (SVR), and Random Forest (RF) are applied to both datasets. The corresponding speed, accuracy, and privacy concerns are compared between the use of the charging records and station records. Real world data compiled at the outlet level from the UCLA campus parking lots are used. The results show that charging records provide relatively faster prediction while putting customer privacy in jeopardy. Station records provide relatively slower prediction while respecting the customer privacy. In general, we found that both datasets generate comparable prediction error.

Suggested Citation

  • Majidpour, Mostafa & Qiu, Charlie & Chu, Peter & Pota, Hemanshu R. & Gadh, Rajit, 2016. "Forecasting the EV charging load based on customer profile or station measurement?," Applied Energy, Elsevier, vol. 163(C), pages 134-141.
    • Handle: RePEc:eee:appene:v:163:y:2016:i:c:p:134-141
    DOI: 10.1016/j.apenergy.2015.10.184
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261915014348
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2015.10.184?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Iversen, Emil B. & Morales, Juan M. & Madsen, Henrik, 2014. "Optimal charging of an electric vehicle using a Markov decision process," Applied Energy, Elsevier, vol. 123(C), pages 1-12.
    2. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    3. Xu, Zhiwei & Hu, Zechun & Song, Yonghua & Zhao, Wei & Zhang, Yongwang, 2014. "Coordination of PEVs charging across multiple aggregators," Applied Energy, Elsevier, vol. 136(C), pages 582-589.
    4. Bae, Mungyu & Kim, Hwantae & Kim, Eugene & Chung, Albert Yongjoon & Kim, Hwangnam & Roh, Jae Hyung, 2014. "Toward electricity retail competition: Survey and case study on technical infrastructure for advanced electricity market system," Applied Energy, Elsevier, vol. 133(C), pages 252-273.
    5. Krishnamurti, Tamar & Schwartz, Daniel & Davis, Alexander & Fischhoff, Baruch & de Bruin, Wändi Bruine & Lave, Lester & Wang, Jack, 2012. "Preparing for smart grid technologies: A behavioral decision research approach to understanding consumer expectations about smart meters," Energy Policy, Elsevier, vol. 41(C), pages 790-797.
    6. Mathew, Paul A. & Dunn, Laurel N. & Sohn, Michael D. & Mercado, Andrea & Custudio, Claudine & Walter, Travis, 2015. "Big-data for building energy performance: Lessons from assembling a very large national database of building energy use," Applied Energy, Elsevier, vol. 140(C), pages 85-93.
    7. McKenna, Eoghan & Richardson, Ian & Thomson, Murray, 2012. "Smart meter data: Balancing consumer privacy concerns with legitimate applications," Energy Policy, Elsevier, vol. 41(C), pages 807-814.
    Full references (including those not matched with items on IDEAS)

    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. Chou, Jui-Sheng & Gusti Ayu Novi Yutami, I, 2014. "Smart meter adoption and deployment strategy for residential buildings in Indonesia," Applied Energy, Elsevier, vol. 128(C), pages 336-349.
    2. Abolhosseini, Shahrouz & Heshmati, Almas & Altmann, Jörn, 2014. "A Review of Renewable Energy Supply and Energy Efficiency Technologies," IZA Discussion Papers 8145, Institute of Labor Economics (IZA).
    3. Chamaret, Cécile & Steyer, Véronique & Mayer, Julie C., 2020. "“Hands off my meter!” when municipalities resist smart meters: Linking arguments and degrees of resistance," Energy Policy, Elsevier, vol. 144(C).
    4. Zhou, Kaile & Yang, Shanlin, 2015. "A framework of service-oriented operation model of China׳s power system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 719-725.
    5. Balta-Ozkan, Nazmiye & Davidson, Rosemary & Bicket, Martha & Whitmarsh, Lorraine, 2013. "Social barriers to the adoption of smart homes," Energy Policy, Elsevier, vol. 63(C), pages 363-374.
    6. Jacqueline Nicole Adams & Zsófia Deme Bélafi & Miklós Horváth & János Balázs Kocsis & Tamás Csoknyai, 2021. "How Smart Meter Data Analysis Can Support Understanding the Impact of Occupant Behavior on Building Energy Performance: A Comprehensive Review," Energies, MDPI, vol. 14(9), pages 1-23, April.
    7. Christine Milchram & Geerten Van de Kaa & Neelke Doorn & Rolf Künneke, 2018. "Moral Values as Factors for Social Acceptance of Smart Grid Technologies," Sustainability, MDPI, vol. 10(8), pages 1-23, August.
    8. Tsvetanov, Tsvetan, 2022. "The deterring effect of monetary costs on smart meter adoption," Applied Energy, Elsevier, vol. 318(C).
    9. Kowalska-Pyzalska, Anna, 2018. "What makes consumers adopt to innovative energy services in the energy market? A review of incentives and barriers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3570-3581.
    10. Flores, Robert J. & Shaffer, Brendan P. & Brouwer, Jacob, 2017. "Electricity costs for a Level 3 electric vehicle fueling station integrated with a building," Applied Energy, Elsevier, vol. 191(C), pages 367-384.
    11. Radenković, Miloš & Bogdanović, Zorica & Despotović-Zrakić, Marijana & Labus, Aleksandra & Lazarević, Saša, 2020. "Assessing consumer readiness for participation in IoT-based demand response business models," Technological Forecasting and Social Change, Elsevier, vol. 150(C).
    12. Strong, Derek Ryan, 2017. "The Early Diffusion of Smart Meters in the US Electric Power Industry," Thesis Commons 7zprk, Center for Open Science.
    13. Chou, Jui-Sheng & Kim, Changwan & Ung, Thanh-Khiet & Yutami, I Gusti Ayu Novi & Lin, Guo-Tai & Son, Hyojoo, 2015. "Cross-country review of smart grid adoption in residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 192-213.
    14. Le Ray, G. & Pinson, P., 2020. "The ethical smart grid: Enabling a fruitful and long-lasting relationship between utilities and customers," Energy Policy, Elsevier, vol. 140(C).
    15. Flores, Robert J. & Shaffer, Brendan P. & Brouwer, Jacob, 2016. "Electricity costs for an electric vehicle fueling station with Level 3 charging," Applied Energy, Elsevier, vol. 169(C), pages 813-830.
    16. Jin, Ruiyang & Zhou, Yuke & Lu, Chao & Song, Jie, 2022. "Deep reinforcement learning-based strategy for charging station participating in demand response," Applied Energy, Elsevier, vol. 328(C).
    17. Hartley, Peter R. & Medlock, Kenneth B. & Jankovska, Olivera, 2019. "Electricity reform and retail pricing in Texas," Energy Economics, Elsevier, vol. 80(C), pages 1-11.
    18. Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 53(6), pages 286-303, January.
    19. Hayashi, Masayoshi, 2014. "Forecasting welfare caseloads: The case of the Japanese public assistance program," Socio-Economic Planning Sciences, Elsevier, vol. 48(2), pages 105-114.
    20. Natascia Andrenacci & Roberto Ragona & Antonino Genovese, 2020. "Evaluation of the Instantaneous Power Demand of an Electric Charging Station in an Urban Scenario," Energies, MDPI, vol. 13(11), pages 1-19, 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:eee:appene:v:163:y:2016:i:c:p:134-141. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.