Predicting Ramp Events with a Stream-Based HMM Framework

  • Carlos Abreu Ferreira
  • João Gama
  • Vítor Santos Costa
  • Vladimiro Miranda
  • Audun Botterud
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7569)

Abstract

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.

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References

  1. 1.
    Bradford, K., Carpenter, R., Shaw, B.: Forecasting southern plains wind ramp events using the wrf model at 3-km. In: AMS Student Conference (2010)Google Scholar
  2. 2.
    Ferreira, C., Gama, J., Matias, L., Botterud, A., Wang, J.: A survey on wind power ramp forecasting. Tech. Rep. ANL/DIS 10-13, Argonne National Laboratory (2010)Google Scholar
  3. 3.
    Focken, U., Lange, M.: Wind power forecasting pilot project in alberta. Energy & meteo systems, Oldenburg, Germany, GmbH (2008)Google Scholar
  4. 4.
    Freedman, J., Markus, M., Penc, R.: Analysis of west texas wind plant ramp-up and ramp-down events. In: AWS Truewind, LLC, Albany, NY (2008)Google Scholar
  5. 5.
    Greaves, B., Collins, J., Parkes, J., Tindal, A.: Temporal forecast uncertainty for ramp events. Wind Engineering 33(11), 309–319 (2009)CrossRefGoogle Scholar
  6. 6.
    Hanssen, A., Kuipers, W.: On the relationship between the frequency of rain and various meteorological parameters. Mededelingen van de Verhandlungen 81 (1965)Google Scholar
  7. 7.
    Kamath, C.: Understanding wind ramp events through analysis of historical data. In: IEEE PES Transmission and Distribution Conference and Expo., New Orleans, LA, United States (2010)Google Scholar
  8. 8.
    Kusiak, A., Zheng, H.: Prediction of wind farm power ramp rates: A data-mining approach. Journal of Solar Energy Engineering 131 (2009)Google Scholar
  9. 9.
    Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, San Diego, CA (2003)Google Scholar
  10. 10.
    Monteiro, C., Bessa, R., Miranda, V., Botterud, A., Wang, J., Conzelmann, G.: Wind power forecasting: State-of-the-art 2009. Tech. Rep. ANL/DIS 10-1, Argonne National Laboratory (2009)Google Scholar
  11. 11.
    Potter, C.W., Grimit, E., Nijssen, B.: Potential benefits of a dedicated probabilistic rapid ramp event forecast tool. IEEE (2009)Google Scholar
  12. 12.
    Rabiner, L.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2) (1989)Google Scholar
  13. 13.
    Srinivasan, A.: Note on the location of optimal classifiers in n-dimensional roc space. Tech. Rep. PRG-TR-2-99, Oxford University (1999)Google Scholar
  14. 14.
    Zack, J., Young, S., Cote, M., Nocera, J.: Development and testing of an innovative short-term large wind ramp forecasting system. In: Wind Power Conference & Exhibition, Dallas, Texas (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Carlos Abreu Ferreira
    • 1
  • João Gama
    • 2
  • Vítor Santos Costa
    • 3
  • Vladimiro Miranda
    • 4
  • Audun Botterud
    • 5
  1. 1.LIAAD-INESC TEC and ISEPPolytechnic Institute of PortoPortoPortugal
  2. 2.LIAAD-INESC TEC and Faculty of EconomicsUniversity of PortoPortoPortugal
  3. 3.CRACS-INESC TEC and Faculty of SciencesUniversity of PortoPortoPortugal
  4. 4.INESC TEC and Faculty of EngineeringUniversity of PortoPortoPortugal
  5. 5.Argonne National LaboratoryArgonneUSA

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