Time series wind power forecasting based on variant Gaussian process and TLBO

Due to the variability and stochastic nature of wind power system, accurate wind power forecasting has an important role in developing reliable and economic power system operation and control strategies. As wind variability is stochastic, Gaussian Process regression has recently been introduced to capture the randomness of wind energy. However, the disadvantages of Gaussian Process regression include its computation complexity and incapability to adapt to time varying time-series systems. A variant Gaussian Process for time series forecasting is introduced in this study to address these issues. This new method is shown to be capable of reducing computational complexity and increasing prediction accuracy. It is further proved that the forecasting result converges as the number of available data approaches innite. Further, a teaching learning based optimization (TLBO) method is used to train the model and to accelerate
the learning rate. The proposed modelling and optimization method is applied to forecast both the wind power generation of Ireland and that from a single wind farm to show the eectiveness of the proposed method.

Is there really "nothing you can do"? Pathways to enhanced flood-risk preparedness

Whilst policy makers have tended to adopt an ‘information-deficit model’ to bolster levels of flood-risk preparedness primarily though communication strategies promoting awareness, the assumed causal relation between awareness and preparedness is empirically weak. As such, there is a growing interest amongst scholars and policy makers alike to better understand why at-risk individuals are underprepared. In this vein, empirical studies, typically employing quantitative methods, have tended to focus on exploring the extent to which flood-risk preparedness levels vary depending not only on socio-demographic variables, but also (and increasingly so) the perceptual factors that influence flood risk preparedness. This study builds upon and extends this body of research by offering a more solution-focused approach that seeks to identify how pathways to flood-risk preparedness can be opened up. Specifically, through application of a qualitative methodology, we seek to explore how the factors that negatively influence flood-risk preparedness can be addressed to foster a shift towards greater levels of mitigation behaviour. In doing so, we focus our analysis on an urban community in Ireland that is identified as ‘at risk’ of flash flooding and is currently undergoing significant flood relief works. In this regard, the case study offers an interesting laboratory to explore how attitudes towards flood-risk preparedness at the individual level are being influenced within the context of a flood relief scheme that is only partially constructed. In order to redress the dearth of theoretically informed qualitative studies in this field, we draw on Protection Motivation Theory (PMT) to help guide our analysis and make sense of our results. Our findings demonstrate that flood-risk preparedness can be undermined by low levels of efficacy amongst individuals in terms of the preparedness measures available to them and their own personal capacity to implement them. We also elucidate that the ‘levee effect’ can occur before engineered flood defences are fully constructed as the flood relief works within our case study are beginning to affect people’s perception of flood risk in the case study area. We conclude by arguing that 1) individuals’ coping appraisals need to be enhanced through communication strategies and other interventions which highlight that future floods may not replicate past events; and 2) the concept of residual risk needs to be communicated at all stages of a flood relief scheme, not just upon completion.