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Article

Subseasonal-to-Seasonal Forecasting for Wind Turbine Maintenance Scheduling

1
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK
2
School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8QQ, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Gregor Giebel
Wind 2022, 2(2), 260-287; https://doi.org/10.3390/wind2020015
Received: 22 March 2022 / Revised: 29 April 2022 / Accepted: 6 May 2022 / Published: 12 May 2022
Certain wind turbine maintenance tasks require specialist equipment, such as a large crane for heavy lift operations. Equipment hire often has a lead time of several weeks but equipment use is restricted by future weather conditions through wind speed safety limits, necessitating an assessment of future weather conditions. This paper sets out a methodology for producing subseasonal-to-seasonal (up to 6 weeks ahead) forecasts that are site- and task-specific. Forecasts are shown to improve on climatology at all sites, with fair skill out to six weeks for both variability and weather window forecasts. For the case of crane hire, a cost-loss model identifies the range of electricity prices where the hiring decision is sensitive to the forecasts. While there is little difference in the hiring decision made by the proposed forecasts and the climatology benchmark at most electricity prices, the repair cost per turbine is reduced at lower electricity prices. View Full-Text
Keywords: subseasonal-to-seasonal; forecasting; maintenance; planning/scheduling; cost loss subseasonal-to-seasonal; forecasting; maintenance; planning/scheduling; cost loss
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MDPI and ACS Style

Tawn, R.; Browell, J.; McMillan, D. Subseasonal-to-Seasonal Forecasting for Wind Turbine Maintenance Scheduling. Wind 2022, 2, 260-287. https://doi.org/10.3390/wind2020015

AMA Style

Tawn R, Browell J, McMillan D. Subseasonal-to-Seasonal Forecasting for Wind Turbine Maintenance Scheduling. Wind. 2022; 2(2):260-287. https://doi.org/10.3390/wind2020015

Chicago/Turabian Style

Tawn, Rosemary, Jethro Browell, and David McMillan. 2022. "Subseasonal-to-Seasonal Forecasting for Wind Turbine Maintenance Scheduling" Wind 2, no. 2: 260-287. https://doi.org/10.3390/wind2020015

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