Operational Evaluation of a Wind-farm Forecasting System

Enda O'Brien; Adam Ralph

doi:10.1016/j.egypro.2015.07.903

Operational Evaluation of a Wind-farm Forecasting System

Enda O'Brien
, Adam Ralph

University of Galway

Research output: Contribution to a Journal (Peer & Non Peer) › Conference article › peer-review

1 Citation (Scopus)

Abstract

Performance of a wind-forecasting system for a wind-farm in Ireland is reported. Forecasts were based on ensembles constructed from HARMONIE model runs every 6 hours, along with extra high-resolution HARMONIE runs every 12 hours. Statistical post-processing with Bayes Model Averaging (BMA) removed bias very effectively. The "raw" incremental skill provided by each extra ensemble member was negligible, but the net value, after BMA post-processing, was significantly larger. Thus, a small ensemble with BMA is more skillful than a larger ensemble with simple averaging only. A larger ensemble is still more skillful than a smaller one, if both use BMA.

Original language	English
Pages (from-to)	216-222
Number of pages	7
Journal	Energy Procedia
Volume	76
DOIs	https://doi.org/10.1016/j.egypro.2015.07.903
Publication status	Published - 2015
Externally published	Yes
Event	European Geosciences Union General Assembly, EGU 2015 - Vienna, Austria Duration: 12 Apr 2015 → 17 Apr 2015

Keywords

Bayes Model Averaging
Mean Absolute Error
Wind-farm

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.egypro.2015.07.903

Cite this

@article{3b0b23df027b4d9f982ad00d9cb69092,

title = "Operational Evaluation of a Wind-farm Forecasting System",

abstract = "Performance of a wind-forecasting system for a wind-farm in Ireland is reported. Forecasts were based on ensembles constructed from HARMONIE model runs every 6 hours, along with extra high-resolution HARMONIE runs every 12 hours. Statistical post-processing with Bayes Model Averaging (BMA) removed bias very effectively. The {"}raw{"} incremental skill provided by each extra ensemble member was negligible, but the net value, after BMA post-processing, was significantly larger. Thus, a small ensemble with BMA is more skillful than a larger ensemble with simple averaging only. A larger ensemble is still more skillful than a smaller one, if both use BMA.",

keywords = "Bayes Model Averaging, Mean Absolute Error, Wind-farm",

author = "Enda O'Brien and Adam Ralph",

note = "Publisher Copyright: {\textcopyright} 2015 The Authors.; European Geosciences Union General Assembly, EGU 2015 ; Conference date: 12-04-2015 Through 17-04-2015",

year = "2015",

doi = "10.1016/j.egypro.2015.07.903",

language = "English",

volume = "76",

pages = "216--222",

journal = "Energy Procedia",

issn = "1876-6102",

publisher = "Elsevier Ltd",

}

TY - JOUR

T1 - Operational Evaluation of a Wind-farm Forecasting System

AU - O'Brien, Enda

AU - Ralph, Adam

PY - 2015

Y1 - 2015

N2 - Performance of a wind-forecasting system for a wind-farm in Ireland is reported. Forecasts were based on ensembles constructed from HARMONIE model runs every 6 hours, along with extra high-resolution HARMONIE runs every 12 hours. Statistical post-processing with Bayes Model Averaging (BMA) removed bias very effectively. The "raw" incremental skill provided by each extra ensemble member was negligible, but the net value, after BMA post-processing, was significantly larger. Thus, a small ensemble with BMA is more skillful than a larger ensemble with simple averaging only. A larger ensemble is still more skillful than a smaller one, if both use BMA.

AB - Performance of a wind-forecasting system for a wind-farm in Ireland is reported. Forecasts were based on ensembles constructed from HARMONIE model runs every 6 hours, along with extra high-resolution HARMONIE runs every 12 hours. Statistical post-processing with Bayes Model Averaging (BMA) removed bias very effectively. The "raw" incremental skill provided by each extra ensemble member was negligible, but the net value, after BMA post-processing, was significantly larger. Thus, a small ensemble with BMA is more skillful than a larger ensemble with simple averaging only. A larger ensemble is still more skillful than a smaller one, if both use BMA.

KW - Bayes Model Averaging

KW - Mean Absolute Error

KW - Wind-farm

UR - https://www.scopus.com/pages/publications/84947231889

U2 - 10.1016/j.egypro.2015.07.903

DO - 10.1016/j.egypro.2015.07.903

M3 - Conference article

SN - 1876-6102

VL - 76

SP - 216

EP - 222

JO - Energy Procedia

JF - Energy Procedia

T2 - European Geosciences Union General Assembly, EGU 2015

Y2 - 12 April 2015 through 17 April 2015

ER -

Operational Evaluation of a Wind-farm Forecasting System

Abstract

Keywords

UN SDGs

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