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

到世纪末，人类活动产生的温室气体排放必须接近于零，以稳定气候。全球能源供应部门是这些排放的最大单一来源（约35%），因此其深度脱碳势在必行。风力发电是最有前途的技术之一，因为它具有广泛的可用性，高通用性和成熟的供应链。管理风力发电的可变性和不稳定性是一个关键的挑战，需要更好的风力发电预测。一般来说，风力发电传统上使用两种方法进行预测：1）通过代表第一原理的物理方程，或2）通过数据驱动的统计模型，该模型将联合收割机历史风力发电量与相关的气象变量（如风速，风向，气温和气压）相结合。这两种方法在地形和位置的通用性以及准确性方面都有局限性，因此需要新的方法。该研究计划旨在将联合收割机两个领域-物理系统和数据驱动的机器学习建模-结合到一个建模框架中，以便将基础知识转化为管理风电预测和生产的法律。为了实现这一目标，该研究计划将重点开发：1）物理信息神经网络（PINN）的新深度学习架构; 2）用于输入数据特征选择的新优化算法。结合起来，这些努力将产生新的，对大气流体流动和动力学的基本见解。所提出的方法将通过将不完全指定的风速物理方程与数值天气预报（NWP）模型生成的大数据相结合，并从气象塔测量，从而实现更可靠的风力预测。据推测，新的建模框架将提供一个显着的飞跃，在预测精度，可以从根本上更高的集成风力发电在电力系统中。这项研究还将通过对风力发电建模的深入研究，深入了解流体动力学的基本模型。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。

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.