End-to-end wind power modelling: developing physics-informed machine learning models for atmospheric fluid dynamics

端到端风力发电建模：开发大气流体动力学的物理信息机器学习模型

• 批准号: RGPIN-2021-04238
• 负责人: Schell, Kristen
• 金额: $ 1.97万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759528
• 关键词: End; end; wind; power; modelling

By the end of the century, greenhouse gas emissions from human activities must approach near-zero in order to stabilize the climate. The global energy supply sector is the largest single source of these emissions (approximately 35%), rendering its deep decarbonization an imperative. Wind power is one of the most promising technologies to contribute to this, given its extensive availability, high versatility, and mature supply chain. Managing the variability and intermittency of wind power is a key challenge-one that demands better wind power prediction. Broadly, wind power has traditionally been predicted using two approaches: 1) through physical equations that represent first principles, or 2) through data-driven statistical models that combine historical wind power production with associated meteorological variables like wind speed, wind direction, air temperature and pressure. Both of these approaches are limited in their generalizability across terrain and location, as well as their accuracy, hence the need for new methods. This research program seeks to combine two domains-physical systems and data-driven machine learning modeling-into one modeling framework, in order to generate fundamental knowledge into the laws governing wind power forecasting and production. To achieve this goal, the research program will focus on developing: 1) new deep learning architectures for physics-informed neural networks (PINN); and 2) new optimization algorithms for input data feature selection. Combined, these efforts will generate new, fundamental insights into atmospheric fluid flow and dynamics. The proposed approach would enable more reliable wind power prediction, by combining what is known from incompletely specified physical equations of wind speed with big data generated from numerical weather prediction (NWP) models and measured from meteorological towers. It is hypothesized that the new modeling framework will provide a significant leap forward in predictive accuracy that could enable radically higher integration of wind power production in the power system. This research would also generate insights into the fundamental models of fluid dynamics through the intensive study of wind power modeling. The general framework developed for integrating fluid dynamics into deep learning neural networks will also be applicable to a wide range of mechanical, aerospace, biomedical and chemical engineering problems.

到本世纪末，人类活动产生的温室气体排放量必须接近零，才能稳定气候。全球能源供应部门是这些排放的最大单一来源(约占35%)，因此其深度脱碳势在必行。鉴于风力发电的广泛可用性、高通用性和成熟的供应链，风力发电是最有希望为此做出贡献的技术之一。管理风能的可变性和间歇性是一项关键挑战--这需要更好的风能预测。总的来说，风能的预测传统上使用两种方法：1)通过代表基本原理的物理方程，或2)通过数据驱动的统计模型，将历史风电产量与相关的气象变量(如风速、风向、气温和气压)结合起来。这两种方法在跨地形和位置的普适性以及准确性方面都受到限制，因此需要新的方法。这项研究计划寻求将物理系统和数据驱动的机器学习建模这两个领域结合到一个建模框架中，以便为风电预测和生产的规律生成基础知识。为了实现这一目标，该研究计划将专注于开发：1)用于物理信息神经网络(PINN)的新的深度学习结构；以及2)用于输入数据特征选择的新的优化算法。综合起来，这些努力将产生对大气流体流动和动力学的新的、基本的见解。拟议的方法将通过将从不完全指定的风速物理方程中获得的信息与数值天气预报(NWP)模型生成并从气象塔测量的大数据相结合，从而实现更可靠的风能预测。假设新的建模框架将在预测精度方面实现重大飞跃，从而能够从根本上提高电力系统中风电生产的集成度。这项研究还将通过深入研究风力发电模型来深入了解流体力学的基本模型。为将流体动力学集成到深度学习神经网络中而开发的通用框架也将适用于广泛的机械、航空航天、生物医学和化学工程问题。