Descriptive Analytics-Based Anomaly Detection for Cybersecure Load Forecasting

Journal Article · Fri Nov 01 00:00:00 EDT 2019 · IEEE Transactions on Smart Grid

DOI:https://doi.org/10.1109/TSG.2019.2894334· OSTI ID:1601349

Yue, Meng ^[1]; Hong, Tao ^[2]; Wang, Jianhui ^[3]

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1. Brookhaven National Lab. (BNL), Upton, NY (United States)
2. Univ. of North Carolina, Charlotte, NC (United States)
3. Southern Methodist Univ., Dallas, TX (United States)

As power delivery systems evolve and become increasingly reliant on accurate forecasts, they will be more and more vulnerable to cybersecurity issues. A coordinated data attack by sophisticated adversaries can render existing data corrupt or outlier detection methods ineffective. This would have a very negative impact on operational decisions. The focus of this paper is to develop descriptive analytics-based methods for anomaly detection to protect the load forecasting process against cyberattacks to essential data. We propose an integrated solution (IS) and a hybrid implementation of IS (HIIS) that can detect and mitigate cyberattack induced long sequence anomalies. HIIS is also capable of improving true positive rates and reducing false positive rates significantly comparing with IS. The proposed HIIS can serve as an online cybersecure load forecasting scheme.

• Research Organization: Brookhaven National Laboratory (BNL), Upton, NY (United States)
• Sponsoring Organization: USDOE Office of Electricity Delivery and Energy Reliability (OE), Power Systems Engineering Research and Development (OE-10)
• Grant/Contract Number: SC0012704
• OSTI ID: 1601349
• Report Number(s): BNL-213641-2020-JAAM
• Journal Information: IEEE Transactions on Smart Grid, Journal Name: IEEE Transactions on Smart Grid Journal Issue: 6 Vol. 10; ISSN 1949-3053
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
Computer crime
Forecasting
Load forecasting
Load modeling
Predictive models
Weather forecasting