Robustness of Short-Term Wind Power Forecasting Against False Data Injection Attacks

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ID: 158414
2020
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Abstract
The accuracy of wind power forecasting depends a great deal on the data quality, which is so susceptible to cybersecurity attacks. In this paper, we study the cybersecurity issue of short-term wind power forecasting. We present one class of data attacks, called false data injection attacks, against wind power deterministic and probabilistic forecasting. We show that any malicious data can be injected to historical data without being discovered by one of the commonly-used anomaly detection techniques. Moreover, we testify that attackers can launch such data attacks even with limited resources. To study the impact of data attacks on the forecasting accuracy, we establish the framework of simulating false data injection attacks using the Monte Carlo method. Then, the robustness of six representative wind power forecasting models is tested. Numerical results on real-world data demonstrate that the support vector machine and k-nearest neighbors combined with kernel density estimator are the most robust deterministic and probabilistic forecasting ones among six representative models, respectively. Nevertheless, none of them can issue accurate forecasts under very strong false data attacks. This presents a serious challenge to the community of wind power forecasting. The challenge is to study robust wind power forecasting models dealing with false data attacks.
Reference Key
zhang2020robustnessenergies Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhang, Yao;Lin, Fan;Wang, Ke;
Journal energies
Year 2020
DOI
DOI not found
URL
https://www.mdpi.com/1996-1073/13/15/3780
Keywords
Technology

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