4. Centros Médicos Colmédica
4.6 Políticas de seguridad del paciente en los Centros Médicos Colmédica
Compared to other climatic parameters (e.g. temperature, precipitation,…), wind speed has received only little attention in climate research. This offers interesting opportunities for further investigation in the research on wind climate and consequent energy generation.
Since the largest capacity is planned to be installed offshore, the most relevant and urgent need for research relates to the power change estimations at offshore locations. The study of Bartstad et al. (2012), focusing on Northern Europe, indicates a small, but uncertain, reduction of off shore wind power, from a downscaled GCM ensemble.
Secondly, focus should be given to the extreme wind speeds under future climate and their effect on the power estimations. Regional climate models have indicated enhanced extreme wind speeds over Western Europe in future climate (e.g. Beniston et al., 2007). Since these gust winds can lead to large loads on the turbine (causing fatigue) and unnecessary turbine shut-downs, they are likely to affect the power under future climate conditions. The Weibull PDF based approach, as applied in this dissertation, fails to describe the extreme upper tail of the distribution, representing the gusts. A statistical technique to account for the gusts would be to fit upper wind speed data in an extreme value distribution of type I (Gumbel distribution) (Sarkar, et al., 2011). A logical next step in this research would be to compare the statistical approach, as developed in this work, with a dynamical approach. A comparison with a dynamical approach will give insights in the applicability of each technique for the prediction of the hub-height winds. It would also provide a more overall assessment of the uncertainty of the projections (Chen et al., 2010). Moreover, it should be stressed that apart from the statistical regression technique applied in this dissertation, many more statistical downscaling techniques have shown to be appropriate in other applications, and could be investigated.
To the authors knowledge, no literature exists about the effect of changes in land use on the hub- height wind speeds, even though historic land use changes are considered crucial in explaining the observed decreasing trend in near-surface winds. Vautard et al. (2010) suggests that up to 60% of the decrease in 10m wind speeds could be explained by an increase in surface roughness (by e.g. forestation, urbanization, etc.). Still, it is unclear whether land use changes alter hub- height wind speeds. From this perspective, a dynamical high resolution downscaling is necessary
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to study the effect of (potential future) changes in surface roughness on the turbine power output.
More in general, we want to stress the need for a strongly increased recognition of the importance of ensemble modeling in climate predictions. The possibilities, limitations and opportunities of different methodologies of ensemble-modeling should be further investigated. The exploration of how to understand, assess and reason about uncertainty in climate projections will require greater collaboration and understanding between climate scientists and statisticians. At last, it is important to point out that the quality of today’s research on hub-height wind climate is largely affected by the lack of available long-term hub-height wind speed measurements, which are necessary to validate the models results. Therefore we would like to encourage the wind power industries to make their time series of wind mast measurements available to the science community. By sharing data and knowledge, everyone will achieve more.
8.
Chapter 8
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