Descriptive Analytics Based Anomaly Detection for Cybersecure Load Forecasting

Data quality has been a big challenge in load forecasting practice, but an underestimated issue in the academic literature. This work was supported by the U.S. Department of Energy through the Cybersecurity for Energy Delivery Systems Program. We were trying to detect anomalies so that accurate load forecasts can be produced even when the data is contaminated.

Citation

Meng Yue, Tao Hong, and Jianhui Wang, "Descriptive analytics based anomaly detection for cybersecure load forecasting," IEEE Transactions on Smart Grid, vol. 10, no. 6, pp. 5964-5974, November, 2019

Descriptive Analytics Based Anomaly Detection for Cybersecure Load Forecasting

Meng Yue, Tao Hong, and Jianhui Wang

Abstract

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.