Rapid Tidal Flow Forecasting for Marine Energy Resource Assessment

海洋能源资源评估的快速潮流预报

• 批准号: NE/S005811/1
• 负责人: Jeff Polton
• 金额: $ 1.73万
• 依托单位: NATIONAL OCEANOGRAPHY CENTRE
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FS005811%2F1
• 关键词: Rapid; Tidal; Flow; Forecasting; Marine

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.

开发潮汐涡轮机等可再生能源需要对潜在的项目地点进行深入评估，以了解其适用性、潜在的能源生产以及对环境的影响。一个地点的可利用性主要是通过广泛的实地调查和数值模拟相结合来评估的，这是昂贵的。由于预算限制，关键的财务和技术决策是在有限的数据样本上做出的，导致高度的风险和不确定性。在这里，我们的目标是通过融合已建立的潮汐流分析技术和机器学习工具来缓解数据稀缺的问题。这个新工具将从经过验证的测量数据中“学习”到在时间上扩展短时间空间调查数据的最佳方法，从而绘制潮汐势图，从而可以直接为更具空间针对性的调查提供信息，或者评估潮汐流设备的最佳位置。该工具旨在通过有针对性地调整调查活动和对数据进行更有力的分析，使调查潜在地点的成本比目前的做法更低。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，以便通过NERC后续电话进一步开发。

项目成果

Bathymetric Influence on the Coastal Sea Level Response to Ocean Gyres at Western Boundaries

• DOI: 10.1175/jpo-d-18-0007.1
• 发表时间: 2018-12
• 期刊: Journal of Physical Oceanography
• 影响因子: 3.5
• 作者: A. Wise;C. Hughes;J. Polton

Estimation of global coastal sea level extremes using neural networks

• DOI: 10.1088/1748-9326/ab89d6
• 发表时间: 2020-07-01
• 期刊: ENVIRONMENTAL RESEARCH LETTERS
• 影响因子: 6.7
• 作者: Bruneau, Nicolas;Polton, Jeff;Holt, Jason
• 通讯作者: Holt, Jason