Robust regression for electricity demand forecasting against cyberattacks
Journal article
VandenHeuvel, Daniel, Wu, Jinran and Wang, You-Gan. (2023). Robust regression for electricity demand forecasting against cyberattacks. International Journal of Forecasting. 39(4), pp. 1573-1592. https://doi.org/10.1016/j.ijforecast.2022.10.004
Authors | VandenHeuvel, Daniel, Wu, Jinran and 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. |
Keywords | robust estimate; data-driven; outliers; regression; cyberattack; data integrity |
Year | 2023 |
Journal | International Journal of Forecasting |
Journal citation | 39 (4), pp. 1573-1592 |
Publisher | Elsevier B.V. |
ISSN | 0169-2070 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.ijforecast.2022.10.004 |
Scopus EID | 2-s2.0-85145217993 |
Page range | 1573-1592 |
Funder | Australian Research Council (ARC) |
Publisher's version | License All rights reserved File Access Level Controlled |
Output status | Published |
Publication dates | |
Online | 10 Dec 2022 |
Publication process dates | |
Deposited | 07 Nov 2023 |
ARC Funded Research | This output has been funded, wholly or partially, under the Australian Research Council Act 2001 |
Grant ID | DP160104292 |
IC190100020 |
https://acuresearchbank.acu.edu.au/item/8zy6y/robust-regression-for-electricity-demand-forecasting-against-cyberattacks
Restricted files
Publisher's version
69
total views0
total downloads4
views this month0
downloads this month