Spatio-temporal prediction of wind speed and direction by continuous directional regime

Research output: Contribution to conferencePaper

4 Citations (Scopus)

Abstract

This paper proposes a statistical method for 1-6 hour-ahead prediction of hourly mean wind speed and direction to better forecast the power produced by wind turbines, an increasingly important component of power system operation. The wind speed and direction are modelled via the magnitude and phase of a complex vector containing measurements from multiple geographic locations. The predictor is derived from the spatio-temporal covariance which is estimated at regular time intervals from a subset of the available training data, the wind direction of which lies within a sliding range of angles centred on the most recent measurement of wind direction. This is a generalisation of regime-switching type approaches which train separate predictors for a few fixed regimes. The new predictor is tested on the Hydra dataset of wind across the Netherlands and compared to persistence and a cyclo-stationary Wiener filter, a state-of-the-art spatial predictor of wind speed and direction. Results show that the proposed technique is able to predict the wind vector more accurately than these benchmarks on dataset containing 4 to 27 sites, with greater accuracy for larger datasets.
Original languageEnglish
Pages1-5
Number of pages5
DOIs
Publication statusPublished - Jul 2014
Event2014 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2014 - Durham, United Kingdom
Duration: 7 Jul 201410 Jul 2014
https://www.dur.ac.uk/dei/events/pmaps2014/

Conference

Conference2014 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2014
Abbreviated titlePMAPS 2014
CountryUnited Kingdom
CityDurham
Period7/07/1410/07/14
Internet address

Keywords

  • load forecasting
  • spatiotemporal phenomena
  • wind turbines
  • complex vector phase
  • power system operation

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  • Projects

    Prizes

    Outstanding Student Paper Award

    Jethro Browell (Recipient), 2014

    Prize: Prize (including medals and awards)

  • Cite this

    Dowell, J., Weiss, S., & Infield, D. (2014). Spatio-temporal prediction of wind speed and direction by continuous directional regime. 1-5. Paper presented at 2014 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2014, Durham, United Kingdom. https://doi.org/10.1109/PMAPS.2014.6960596