Predicting Ramp Events with a Stream-Based HMM Framework

• Type: Article in International Conference Proceedings Book
• Date: 2012

Authors

Ferreira, CA (Author) Other; Gama, J (Author) FEP; Santos Costa, V (Author) FCUP; Miranda, V (Author) FEUP; Botterud, A (Author) Other

Conference proceedings International

Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Pages: 224-238

15th International Conference on Discovery Science, DS 2012

Lyon, 29 October 2012 through 31 October 2012

Indexing

Scopus - 13 Citations

Other information

Authenticus ID: P-008-729

DOI: 10.1007/978-3-642-33492-4_19

Abstract (EN): 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 SHRED 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 historical data is obtained by running the SAX algorithm over the first order variations in the original signal. SHRED updates the HMM using the most recent historical data and includes a forgetting mechanism to model natural time dependence in wind patterns. To forecast ramp events we use recent wind speed forecasts and the Viterbi algorithm, that incrementally finds the most probable ramp event to occur. We compare SHRED framework against Persistence baseline in predicting ramp events occurring in short-time horizons, ranging from 30 minutes to 90 minutes. SHRED consistently exhibits more accurate and cost-effective results than the baseline. © 2012 Springer-Verlag Berlin Heidelberg.

Language: English

Type (Professor's evaluation): Scientific