Machine Learning-Based Anomaly Detection for Load Forecasting Under Cyberattacks [electronic resource]
Accurate load forecasting can create both economic and reliability benefits for power system operators. However, the cyberattack on load forecasting may mislead operators to make unsuitable operational decisions for the electricity delivery. To effectively and accurately detect these cyberattacks, t...
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Full Text (via OSTI) |
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| Format: | Government Document Electronic eBook |
| Language: | English |
| Published: |
Upton, N.Y. : Oak Ridge, Tenn. :
Brookhaven National Laboratory ; Distributed by the Office of Scientific and Technical Information, U.S. Department of Energy,
2019.
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| Subjects: |
MARC
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| 245 | 0 | 0 | |a Machine Learning-Based Anomaly Detection for Load Forecasting Under Cyberattacks |h [electronic resource] |
| 260 | |a Upton, N.Y. : |b Brookhaven National Laboratory ; |a Oak Ridge, Tenn. : |b Distributed by the Office of Scientific and Technical Information, U.S. Department of Energy, |c 2019. | ||
| 300 | |a Size: p. 5724-5734 : |b digital, PDF file. | ||
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| 500 | |a 01/03/2019. | ||
| 500 | |a "bnl-212334-2019-jaam" | ||
| 500 | |a "Journal ID: ISSN 1949-3053" | ||
| 500 | |a Cui, Mingjian ; Wang, Jianhui ; Yue, Meng ; | ||
| 500 | |a USDOE Office of Science (SC), Advanced Scientific Computing Research (SC-21) | ||
| 520 | 3 | |a Accurate load forecasting can create both economic and reliability benefits for power system operators. However, the cyberattack on load forecasting may mislead operators to make unsuitable operational decisions for the electricity delivery. To effectively and accurately detect these cyberattacks, this paper develops a machine learning-based anomaly detection (MLAD) methodology. First, load forecasts provided by neural networks are used to reconstruct the benchmark and scaling data by using the k-means clustering. Second, the cyberattack template is estimated by the naive Bayes classification based on the cumulative distribution function and statistical features of the scaling data. Finally, the dynamic programming is utilized to calculate both the occurrence and parameter of one cyberattack on load forecasting data. Here, a widely used symbolic aggregation approximation method is compared with the developed MLAD method. Numerical simulations on the publicly load data show that the MLAD method can effectively detect cyberattacks for load forecasting data with relatively high accuracy. Also, the robustness of MLAD is verified by thousands of attack scenarios based on Monte Carlo simulation. | |
| 536 | |b SC0012704. | ||
| 650 | 7 | |a 97 mathematics and computing |2 local. | |
| 650 | 7 | |a Anomaly detection |2 local. | |
| 650 | 7 | |a Cyberattack |2 local. | |
| 650 | 7 | |a Dynamic programming |2 local. | |
| 650 | 7 | |a Load forecasting |2 local. | |
| 650 | 7 | |a Machine learning |2 local. | |
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| 710 | 1 | |a United States. |b Department of Energy. |b Office of Scientific and Technical Information |4 dst. | |
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