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Robust regression for electricity demand forecasting against cyberattacks

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  • VandenHeuvel, Daniel
  • Wu, Jinran
  • Wang, You-Gan

Abstract

Standard methods for forecasting electricity loads are not robust to cyberattacks on electricity demand data, potentially leading to severe consequences such as major economic loss or a system blackout. Methods are required that can handle forecasting under these conditions and detect outliers that would otherwise go unnoticed. The key challenge is to remove as many outliers as possible while maintaining enough clean data to use in the regression. In this paper we investigate robust approaches with data-driven tuning parameters, and in particular present an adaptive trimmed regression method that can better detect outliers and provide improved forecasts. In general, data-driven approaches perform much better than their fixed tuning parameter counterparts. Recommendations for future work are provided.

Suggested Citation

  • VandenHeuvel, Daniel & Wu, Jinran & Wang, You-Gan, 2023. "Robust regression for electricity demand forecasting against cyberattacks," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1573-1592.
    • Handle: RePEc:eee:intfor:v:39:y:2023:i:4:p:1573-1592
    DOI: 10.1016/j.ijforecast.2022.10.004
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    1. Pierre Pinson & Liyang Han & Jalal Kazempour, 2022. "Regression markets and application to energy forecasting," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 533-573, October.
    2. 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.
    3. Jiang, Ping & Li, Ranran & Liu, Ningning & Gao, Yuyang, 2020. "A novel composite electricity demand forecasting framework by data processing and optimized support vector machine," Applied Energy, Elsevier, vol. 260(C).
    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. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    6. Gaillard, Pierre & Goude, Yannig & Nedellec, Raphaël, 2016. "Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1038-1050.
    7. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    8. 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.
    9. Dudek, Grzegorz, 2016. "Multilayer perceptron for GEFCom2014 probabilistic electricity price forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1057-1060.
    10. Jasiūnas, Justinas & Lund, Peter D. & Mikkola, Jani & Koskela, Liinu, 2021. "Linking socio-economic aspects to power system disruption models," Energy, Elsevier, vol. 222(C).
    11. Kanda, Isao & Veguillas, J.M. Quintana, 2019. "Data preprocessing and quantile regression for probabilistic load forecasting in the GEFCom2017 final match," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1460-1468.
    12. Haeran Cho & Yannig Goude & Xavier Brossat & Qiwei Yao, 2013. "Modeling and Forecasting Daily Electricity Load Curves: A Hybrid Approach," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 7-21, March.
    13. Tao Hong, 2014. "Energy Forecasting: Past, Present, and Future," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 32, pages 43-48, Winter.
    14. Bedi, Jatin & Toshniwal, Durga, 2019. "Deep learning framework to forecast electricity demand," Applied Energy, Elsevier, vol. 238(C), pages 1312-1326.
    15. Zeng, Zijian & Li, Meng, 2021. "Bayesian median autoregression for robust time series forecasting," International Journal of Forecasting, Elsevier, vol. 37(2), pages 1000-1010.
    16. Lu, Hongfang & Ma, Xin & Ma, Minda, 2021. "A hybrid multi-objective optimizer-based model for daily electricity demand prediction considering COVID-19," Energy, Elsevier, vol. 219(C).
    17. Neykov, N. & Filzmoser, P. & Dimova, R. & Neytchev, P., 2007. "Robust fitting of mixtures using the trimmed likelihood estimator," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 299-308, September.
    18. Hannah E. Correia & Asheber Abebe, 2021. "Regularised rank quasi-likelihood estimation for generalised additive models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 33(1), pages 101-117, January.
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