AI Based Tools for Subsea Cable Routing for Offshore Wind Farms

Cable routing is a non-trivial aspect of offshore wind farm development. An optimal route must balance multiple factors such as thermal efficiency, soil type, benthic communities, shipping traffic, fishing zones, and more. Inadequate consideration of these factors can lead to severe financial and environmental consequences.

Financially, cable failures are projected to cost €61.5 billion globally over the next decade (TGS Energy Data & Intelligence, 2024). Environmentally, subsea cables can disrupt habitats and benthic ecosystems, and their electromagnetic fields (EMF) have been shown to affect certain marine species. With the UK’s strategic expansion of offshore wind, route planning needs to be robust, environmentally responsible, and transparent, including the ability to provide detailed explanations for agencies such as The Crown Estate.

However, current cable route planning relies on a combination of GIS tools, surveying, optimisation, and past experience. While effective for small projects, these methods become increasingly limited as offshore wind farms continue to grow larger, extend further offshore, and generate more power. Planners must synthesise vast multidisciplinary knowledge across geotechnical engineering, marine ecology, and maritime traffic, which may result in cognitive overload and subjective biases.

Additionally, route planners may lack awareness of newly emerging ecological findings, such as EMF sensitivity in local marine species. The overall goal of this PhD is to develop novel AI-based systems that can help planners develop efficient (cost-effective), environmentally friendly (e.g. minimising disruption to shipping traffic and avoiding marine conservation zones), thermally efficient (e.g. accounting for the impact of burial depth and soil composition on cable thermal performance), and easy-to-deploy cable routes (e.g. where the seabed is conducive to cable installation at the appropriate depth). The student will join the Language and Data Research Group at Loughborough University.

The student will also have the opportunity to interact with scientists at AURA CDT ( The EPSRC Centre for Doctoral Training in Offshore Wind Energy Sustainability and Resilience ). AURA is a multi-institution, transdisciplinary centre for doctoral training established by EPSRC, UKRI. The consortium consists of leading scientists and academics in offshore wind energy from four UK higher education institutions, including Loughborough University, as well as dozens of relevant industry partners.

The overall goal of the CDT is to train and mentor future leaders in offshore wind energy in the UK. The primary supervisor, Dr Gunturi, is a Lecturer in the Department of Computer Science with over 10 years of experience in geospatial data systems and data science. He has worked extensively on scalability and semantic aspects of answering routing queries on geospatial datasets.

Prof Dethlefs is a Professor of Computer Science (Artificial Intelligence) at Lou