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

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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.