Descriptive Analytics-Based Anomaly Detection for Cybersecure Load Forecasting
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 Lab. (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, Vol. 10, Issue 6; ISSN 1949-3053
- Publisher:
- IEEECopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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