Rapid Tidal Flow Forecasting for Marine Energy Resource Assessment

开发潮汐涡轮机等可再生能源需要对潜在项目地点进行深入评估，以了解适宜性、潜在能源生产以及对环境的影响。一个场地的可开采性主要是通过广泛的实地调查和数值模拟相结合的方式来评估的，这是昂贵的。由于预算的限制，关键的财务和技术决策是在有限的数据样本上做出的，这导致了高水平的风险和不确定性。在这里，我们的目标是通过将已建立的潮流分析技术与机器学习工具相融合来缓解数据稀缺的问题。新工具将从经过验证的量规数据中“学习”在时间上扩展短持续时间空间测量数据的最佳方法，以制作潮汐潜势图，从而直接为更具空间针对性的测量或潮汐水流装置的最佳位置评估提供信息。该工具旨在通过有针对性地调整调查活动和对数据进行比目前更可靠的分析，使调查潜在地点的成本更低。这是一项概念验证研究。结果包括评估该工具是否具有足够的商业价值，可以通过NERC的后续呼吁进一步开发。

Developing renewable energy such as tidal turbines requires in-depth assessment of a potential project site to understand suitability, potential energy production as well as impact on the environment. The exploitability of a site is mainly assessed by a combination of extensive field surveys with numerical modelling, which is expensive. Due to budget limitations, critical financial and technical decisions are made on a restricted sample of data leading to high level of risk and uncertainties.Here we aim to mitigate the issue of data scarcity by fusing established tidal flow analysis techniques with machine learning tools. The new tool will 'learn', from verified gauge data, the best way to temporally extend short-duration spatial survey data to make maps of tidal potential that can directly inform either more spatially targeted surveying, or assessments for optimal siting of tidal stream devices. The tool aims to make surveying potential sites cheaper by targeted adaption of the survey campaign and more robust analysis of the data than is currently practiced.This is a proof-of-concept study. The outcomes include assessing whether the tool has sufficient commercial merit to be developed further via a NERC follow-on call.