A hierarchical approach to probabilistic wind power forecasting

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

1 Citation (Scopus)
2 Downloads (Pure)

Abstract

This paper describes a method to generate improved probabilistic wind farm power forecasts in a hierarchical framework with the incorporation of production data from individual wind turbines. Wind power forms a natural hierarchy as generated electricity is aggregated from the individual turbine, to farm, to the regional level and so on. To forecast the wind farm power generation, a layered approach is proposed whereby deterministic forecasts from the lower layer (turbine level) are used as input features to an upper-level (wind farm) probabilistic model. In a case study at a utility scale wind farm it is shown that improvements in probabilistic forecast skill (CRPS) of 1.24% and 2.39% are obtainable when compared to two very competitive benchmarks based on direct forecasting of the wind farm power using Gradient Boosting Trees and an Analog Ensemble, respectively.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages6
ISBN (Print)9781538635964
DOIs
Publication statusE-pub ahead of print - 20 Aug 2018
Event2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) - Boise, ID, United States
Duration: 24 Jun 201828 Jun 2018

Conference

Conference2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
CountryUnited States
Period24/06/1828/06/18

Fingerprint

Farms
Wind power
Turbines
Wind turbines
Power generation
Electricity

Keywords

  • wind power
  • probabilistic forecasting
  • hierarchical forecasting
  • forecasting
  • wind power integration

Cite this

Gilbert, C., Browell, J., & McMillan, D. (2018). A hierarchical approach to probabilistic wind power forecasting. In 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Piscataway, NJ: IEEE. https://doi.org/10.1109/PMAPS.2018.8440571
Gilbert, Ciaran ; Browell, Jethro ; McMillan, David. / A hierarchical approach to probabilistic wind power forecasting. 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). Piscataway, NJ : IEEE, 2018.
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abstract = "This paper describes a method to generate improved probabilistic wind farm power forecasts in a hierarchical framework with the incorporation of production data from individual wind turbines. Wind power forms a natural hierarchy as generated electricity is aggregated from the individual turbine, to farm, to the regional level and so on. To forecast the wind farm power generation, a layered approach is proposed whereby deterministic forecasts from the lower layer (turbine level) are used as input features to an upper-level (wind farm) probabilistic model. In a case study at a utility scale wind farm it is shown that improvements in probabilistic forecast skill (CRPS) of 1.24{\%} and 2.39{\%} are obtainable when compared to two very competitive benchmarks based on direct forecasting of the wind farm power using Gradient Boosting Trees and an Analog Ensemble, respectively.",
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author = "Ciaran Gilbert and Jethro Browell and David McMillan",
note = "{\circledC} 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.",
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Gilbert, C, Browell, J & McMillan, D 2018, A hierarchical approach to probabilistic wind power forecasting. in 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, Piscataway, NJ, 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), United States, 24/06/18. https://doi.org/10.1109/PMAPS.2018.8440571

A hierarchical approach to probabilistic wind power forecasting. / Gilbert, Ciaran; Browell, Jethro; McMillan, David.

2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). Piscataway, NJ : IEEE, 2018.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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AU - Browell, Jethro

AU - McMillan, David

N1 - © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2018/8/20

Y1 - 2018/8/20

N2 - This paper describes a method to generate improved probabilistic wind farm power forecasts in a hierarchical framework with the incorporation of production data from individual wind turbines. Wind power forms a natural hierarchy as generated electricity is aggregated from the individual turbine, to farm, to the regional level and so on. To forecast the wind farm power generation, a layered approach is proposed whereby deterministic forecasts from the lower layer (turbine level) are used as input features to an upper-level (wind farm) probabilistic model. In a case study at a utility scale wind farm it is shown that improvements in probabilistic forecast skill (CRPS) of 1.24% and 2.39% are obtainable when compared to two very competitive benchmarks based on direct forecasting of the wind farm power using Gradient Boosting Trees and an Analog Ensemble, respectively.

AB - This paper describes a method to generate improved probabilistic wind farm power forecasts in a hierarchical framework with the incorporation of production data from individual wind turbines. Wind power forms a natural hierarchy as generated electricity is aggregated from the individual turbine, to farm, to the regional level and so on. To forecast the wind farm power generation, a layered approach is proposed whereby deterministic forecasts from the lower layer (turbine level) are used as input features to an upper-level (wind farm) probabilistic model. In a case study at a utility scale wind farm it is shown that improvements in probabilistic forecast skill (CRPS) of 1.24% and 2.39% are obtainable when compared to two very competitive benchmarks based on direct forecasting of the wind farm power using Gradient Boosting Trees and an Analog Ensemble, respectively.

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Gilbert C, Browell J, McMillan D. A hierarchical approach to probabilistic wind power forecasting. In 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). Piscataway, NJ: IEEE. 2018 https://doi.org/10.1109/PMAPS.2018.8440571