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Random Forest Based Model for Preventing Large-Scale Emergencies in Power Systems

Nikita Tomin, Aleksei Zhukov, Denis Sidorov, Viktor Kurbatsky, Daniil Panasetsky, Vadim Spiryaev


The novel adaptive hybrid models are proposed for time series forecasting and features classification problems. The proposed forecasting model combines the Hilbert–Huang transform and random forests. The efficiency of proposed adaptive approaches is demonstrated on two cases studies: wind power ramps prediction and detection of alarm states in a power systems.


regression, classification, forecast parameters, power systems, energy efficiency, alarm states detection; Random forest, SVM, Hilbert-Huang transform.

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