Life Cycle Management and Resource Efficiency for Offshore Wind Parks: A Data-driven Approach
PhD candidate: Yucheng Lyu
Offshore wind parks play a critical role in the global energy transition, and the efficient utilization of their resources is essential for enhancing renewable energy capacity and achieving sustainable development. However, their operation still faces multiple challenges, particularly in the data management and utilization. The strategic implementation of data-driven approaches has become increasingly important for optimizing wind park performance and ensuring long-term sustainability. This study focuses on data-related challenges in two major categories of resources within offshore wind parks: (i)the application of data-driven methods to the management of natural resource, and (ii)the use of data-driven approaches in managing artificial resources management. Three key data challenges are identified and analyzed—data skewness and imbalance, distributional shifts, and data sparsity—each of which poses significant obstacles to effective resource management. In response to these challenges, the study explores the use of data-driven techniques in several critical areas, including wind energy forecasting, turbine layout optimization, equipment lifecycle management, and preventive maintenance. These domains are assessed through a data-centric lens, highlighting opportunities for performance improvement. This study underscores the pivotal role of data-driven technologies in addressing resource utilization challenges, enhancing operational efficiency, and supporting both sustainability and the circular economy principles in offshore wind energy systems. This research seeks to establish more effective utilization strategies that align with the goals of sustainable energy, ultimately maximizing both economic and societal returns from offshore wind resources.
