Picture of neon light reading 'Open'

Discover open research at Strathprints as part of International Open Access Week!

23-29 October 2017 is International Open Access Week. The Strathprints institutional repository is a digital archive of Open Access research outputs, all produced by University of Strathclyde researchers.

Explore recent world leading Open Access research content this Open Access Week from across Strathclyde's many research active faculties: Engineering, Science, Humanities, Arts & Social Sciences and Strathclyde Business School.

Explore all Strathclyde Open Access research outputs...

An economic impact metric for evaluating wave height forecasters for offshore wind maintenance access

Catterson, V.M. and McMillan, D. and Dinwoodie, I. and Revie, M. and Dowell, J. and Quigley, J. and Wilson, K. (2016) An economic impact metric for evaluating wave height forecasters for offshore wind maintenance access. Wind Energy, 19 (2). pp. 199-212. ISSN 1095-4244

[img]
Preview
PDF (Catterson-etal-WE-2015-An-economic-impact-metric-for-evaluating-wave-hight)
Catterson_etal_WE_2015_An_economic_impact_metric_for_evaluating_wave_hight.pdf - Accepted Author Manuscript

Download (708kB) | Preview

Abstract

This paper demonstrates that wave height forecasters chosen on statistical quality metrics result in sub-optimal decision support for offshore wind farm maintenance. Offshore access is constrained by wave height, but the majority of approaches to evaluating the effectiveness of a wave height forecaster utilize overall accuracy or error rates. This paper introduces a new metric more appropriate to the wind industry, which considers the economic impact of an incorrect forecast above or below critical wave height boundaries. The paper describes a process for constructing a value criteria where the implications between forecasting error and economic consequences are explicated in terms of opportunity costs and realized maintenance costs. A comparison between nine forecasting techniques for modeling and predicting wave heights based on historical data, including an ensemble aggregator, is described demonstrating that the performance ranking of forecasters is sensitive to the evaluation criteria. The results highlight the importance of appropriate metrics for wave height prediction specific to the wind industry, and the limitations of current models that minimize a metric that does not support decision making. With improved ability to forecast weather windows, maintenance scheduling is subject to less uncertainty, hence reducing costs related to vessel dispatch, and lost energy due to downtime.