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Predicting Ramp Events with a Stream-Based HMM Framework
[chapter]
2012
Lecture Notes in Computer Science
The motivation for this work is the study and prediction of wind ramp events occurring in a large-scale wind farm located in the US Midwest. In this paper we introduce the SHREA framework, a stream-based model that continuously learns a discrete HMM model from wind power and wind speed measurements. We use a supervised learning algorithm to learn HMM parameters from discretized data, where ramp events are HMM states and discretized wind speed data are HMM observations. The discretization of the
doi:10.1007/978-3-642-33492-4_19
fatcat:tmmkl4swp5g3dabbi2aqyismcq