IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v39y2023i2p1005-1020.html
   My bibliography  Save this article

A robust support vector regression model for electric load forecasting

Author

Listed:
  • Luo, Jian
  • Hong, Tao
  • Gao, Zheming
  • Fang, Shu-Cherng

Abstract

Electric load forecasting is a crucial part of business operations in the energy industry. Various load forecasting methods and techniques have been proposed and tested. With growing concerns about cybersecurity and malicious data manipulations, an emerging topic is to develop robust load forecasting models. In this paper, we propose a robust support vector regression (SVR) model to forecast the electricity demand under data integrity attacks. We first introduce a weight function to calculate the relative importance of each observation in the load history. We then construct a weighted quadratic surface SVR model. Some theoretical properties of the proposed model are derived. Extensive computational experiments are based on the publicly available data from Global Energy Forecasting Competition 2012 and ISO New England. To imitate data integrity attacks, we have deliberately increased or decreased the historical load data. Finally, the computational results demonstrate better accuracy of the proposed robust model over other recently proposed robust models in the load forecasting literature.

Suggested Citation

  • Luo, Jian & Hong, Tao & Gao, Zheming & Fang, Shu-Cherng, 2023. "A robust support vector regression model for electric load forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 1005-1020.
  • Handle: RePEc:eee:intfor:v:39:y:2023:i:2:p:1005-1020
    DOI: 10.1016/j.ijforecast.2022.04.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207022000528
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijforecast.2022.04.001?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. Xie, Jingrui & Hong, Tao, 2016. "GEFCom2014 probabilistic electric load forecasting: An integrated solution with forecast combination and residual simulation," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1012-1016.
    2. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
    3. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2015. "Support vector regression for loss given default modelling," European Journal of Operational Research, Elsevier, vol. 240(2), pages 528-538.
    4. Hong, Tao & Pinson, Pierre & Fan, Shu, 2014. "Global Energy Forecasting Competition 2012," International Journal of Forecasting, Elsevier, vol. 30(2), pages 357-363.
    5. Luo, Jian & Yan, Xin & Tian, Ye, 2020. "Unsupervised quadratic surface support vector machine with application to credit risk assessment," European Journal of Operational Research, Elsevier, vol. 280(3), pages 1008-1017.
    6. Hong, Tao & Pinson, Pierre & Fan, Shu & Zareipour, Hamidreza & Troccoli, Alberto & Hyndman, Rob J., 2016. "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond," International Journal of Forecasting, Elsevier, vol. 32(3), pages 896-913.
    7. Sobhani, Masoud & Hong, Tao & Martin, Claude, 2020. "Temperature anomaly detection for electric load forecasting," International Journal of Forecasting, Elsevier, vol. 36(2), pages 324-333.
    8. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    9. Luo, Jian & Hong, Tao & Fang, Shu-Cherng, 2018. "Benchmarking robustness of load forecasting models under data integrity attacks," International Journal of Forecasting, Elsevier, vol. 34(1), pages 89-104.
    10. Spiliotis, Evangelos & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2019. "Tales from tails: On the empirical distributions of forecasting errors and their implication to risk," International Journal of Forecasting, Elsevier, vol. 35(2), pages 687-698.
    11. Jian Luo & Shu-Cherng Fang & Zhibin Deng & Xiaoling Guo, 2016. "Soft Quadratic Surface Support Vector Machine for Binary Classification," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(06), pages 1-22, December.
    12. Hong, Tao & Wang, Pu & White, Laura, 2015. "Weather station selection for electric load forecasting," International Journal of Forecasting, Elsevier, vol. 31(2), pages 286-295.
    13. Charlton, Nathaniel & Singleton, Colin, 2014. "A refined parametric model for short term load forecasting," International Journal of Forecasting, Elsevier, vol. 30(2), pages 364-368.
    14. Akouemo, Hermine N. & Povinelli, Richard J., 2016. "Probabilistic anomaly detection in natural gas time series data," International Journal of Forecasting, Elsevier, vol. 32(3), pages 948-956.
    15. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601, December.
    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. Piao Wang & Shahid Hussain Gurmani & Zhifu Tao & Jinpei Liu & Huayou Chen, 2024. "Interval time series forecasting: A systematic literature review," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 249-285, March.
    2. Feng, Yun & Hou, Weijie & Song, Yuping, 2023. "Tail risk in the Chinese stock market: An AEV model on the maximal drawdowns," Finance Research Letters, Elsevier, vol. 58(PA).

    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. Sobhani, Masoud & Hong, Tao & Martin, Claude, 2020. "Temperature anomaly detection for electric load forecasting," International Journal of Forecasting, Elsevier, vol. 36(2), pages 324-333.
    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. Luo, Jian & Hong, Tao & Fang, Shu-Cherng, 2018. "Benchmarking robustness of load forecasting models under data integrity attacks," International Journal of Forecasting, Elsevier, vol. 34(1), pages 89-104.
    4. Moreno-Carbonell, Santiago & Sánchez-Úbeda, Eugenio F. & Muñoz, Antonio, 2020. "Rethinking weather station selection for electric load forecasting using genetic algorithms," International Journal of Forecasting, Elsevier, vol. 36(2), pages 695-712.
    5. Jingrui Xie & Tao Hong, 2017. "Wind Speed for Load Forecasting Models," Sustainability, MDPI, vol. 9(5), pages 1-12, May.
    6. Jiao, Jieying & Tang, Zefan & Zhang, Peng & Yue, Meng & Yan, Jun, 2022. "Cyberattack-resilient load forecasting with adaptive robust regression," International Journal of Forecasting, Elsevier, vol. 38(3), pages 910-919.
    7. Wang, Pu & Liu, Bidong & Hong, Tao, 2016. "Electric load forecasting with recency effect: A big data approach," International Journal of Forecasting, Elsevier, vol. 32(3), pages 585-597.
    8. Santiago Moreno-Carbonell & Eugenio F. Sánchez-Úbeda & Antonio Muñoz, 2020. "Time Series Decomposition of the Daily Outdoor Air Temperature in Europe for Long-Term Energy Forecasting in the Context of Climate Change," Energies, MDPI, vol. 13(7), pages 1-28, March.
    9. Nowotarski, Jakub & Liu, Bidong & Weron, Rafał & Hong, Tao, 2016. "Improving short term load forecast accuracy via combining sister forecasts," Energy, Elsevier, vol. 98(C), pages 40-49.
    10. Xie, Jingrui & Hong, Tao, 2016. "GEFCom2014 probabilistic electric load forecasting: An integrated solution with forecast combination and residual simulation," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1012-1016.
    11. Haben, Stephen & Giasemidis, Georgios & Ziel, Florian & Arora, Siddharth, 2019. "Short term load forecasting and the effect of temperature at the low voltage level," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1469-1484.
    12. Seyedeh Narjes Fallah & Mehdi Ganjkhani & Shahaboddin Shamshirband & Kwok-wing Chau, 2019. "Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview," Energies, MDPI, vol. 12(3), pages 1-21, January.
    13. Masoud Sobhani & Allison Campbell & Saurabh Sangamwar & Changlin Li & Tao Hong, 2019. "Combining Weather Stations for Electric Load Forecasting," Energies, MDPI, vol. 12(8), pages 1-11, April.
    14. Kamal Chapagain & Somsak Kittipiyakul, 2018. "Performance Analysis of Short-Term Electricity Demand with Atmospheric Variables," Energies, MDPI, vol. 11(4), pages 1-34, April.
    15. Tartakovsky, Alexandre M. & Ma, Tong & Barajas-Solano, David A. & Tipireddy, Ramakrishna, 2023. "Physics-informed Gaussian process regression for states estimation and forecasting in power grids," International Journal of Forecasting, Elsevier, vol. 39(2), pages 967-980.
    16. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    17. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    18. Gao, Zheming & Fang, Shu-Cherng & Luo, Jian & Medhin, Negash, 2021. "A kernel-free double well potential support vector machine with applications," European Journal of Operational Research, Elsevier, vol. 290(1), pages 248-262.
    19. de Hoog, Julian & Abdulla, Khalid, 2019. "Data visualization and forecast combination for probabilistic load forecasting in GEFCom2017 final match," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1451-1459.
    20. Huber, Julian & Dann, David & Weinhardt, Christof, 2020. "Probabilistic forecasts of time and energy flexibility in battery electric vehicle charging," Applied Energy, Elsevier, vol. 262(C).

    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:intfor:v:39:y:2023:i:2:p:1005-1020. 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/locate/ijforecast .

    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.