This page presents an excerpt of the available research material, including the Preliminary Pages, Table of Contents, Abstract, Chapters One to Five, and References. It provides a comprehensive overview of the study, enhancing readability and accessibility for students, and researchers seeking complete material on the topic stated above.
I am profoundly grateful to everyone who contributed to the successful completion of this project. I am especially grateful to my Supervisor (Name), the Head of Department (Name), and the Lecturers in the Department of Statistics for their invaluable guidance and support. I also acknowledge the contributions of authors and scholars whose works on Influence of local wind speed and direction on wind power dynamics Application to offshore very short term forecasting provided essential insights. Special thanks go to my study area (and any funding organizations, if applicable) for their financial assistance. I am equally thankful to stakeholders, including mentors, teachers, and colleagues, for their encouragement and support. Finally, I deeply appreciate my family and friends for their patience and unwavering support throughout this journey. Your contributions have been instrumental in making this research a reality.
Wind power time series usually show complex dynamics mainly due to non-linearities related to the wind physics and the power transformation process in wind farms. This article provides an approach to the incorporation of observed local variables (wind speed and direction) to model some of these effects by means of statistical models. To this end, a bench marking between two different families of varying coefficient models (regime-switching and conditional parametric models) is carried out. The case of the offshore wind farm of Horns Rev in Denmark has been considered. The analysis is focused on one-step ahead forecasting and a time series resolution of 10 min. It has been found that the local wind direction contributes to model some features of the prevailing winds, such as the impact of the wind direction on the wind variability, whereas the non-linearities related to the power transformation process can be introduced by considering the local wind speed. In both cases, conditional parametric models showed a better performance than the one achieved by the regime-switching strategy. The results attained reinforce the idea that each explanatory variable allows the modelling of different underlying effects in the dynamics of wind power time series.
1.1 Introduction
… As a prelude to other parts of this study, this chapter will discuss the background upon which this study was initiated, the statement of problems that led to this study, the aim and objectives of the study. Others are significance of the study, scope of work, Research hypothesis and questions, limitation of the study and definition of terms.…
