# Scientific

## Short-term wind forecasting using spatio-temporal covariance models

This talk introduces a methodology for improving short-term wind speed forecasting in Alberta. Regime-switching spatio-temporal covariance models are applied using two datasets: (1) large-scale reanalysis dataset containing large scale atmospheric information for atmospheric clustering using k-means and hidden Markov models; (2) wind speed data from 131 weather stations across Alberta are used to train and test the covariance models. The predictive performance is assessed for different models and clustering methods.

## Offshore wind forecasting and operations for the offshore wind energy areas in the U.S. Mid Atlantic

The rising U.S. offshore wind sector holds great promise, both environmentally and economically, to unlock vast supplies of clean, domestic, and renewable energy. To harness this valuable resource, Gigawatt (GW)-scale offshore wind projects are already under way at several locations off of the U.S. coastline. This promising future, however, is still clouded with uncertainties on how to optimally manage those ultra-scale offshore wind assets, which would be operating under harsh environmental and operational conditions, in relatively under-explored territories, and at unprecedented scales. I will present some of our progress in formulating tailored forecasting and optimization models aimed at minimizing some of those uncertainties. Our models and analyses are largely tailored and tested using data from the U.S. Mid-Atlantic—where several GW-scale wind projects are currently under development.

## A Tribute to Bill Aiello

A tribute to Bill Aiello

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## The role of wind speed variability in very long-term wind power forecasts

How much wind power will a turbine generate over its lifetime? To answer such questions, we can consider climate model output to generate very long-term wind power forecasts on the scale of years to decades. One major limitation of the data projected by climate models is their coarse temporal resolution that is usually not finer than three hours and can be as coarse as one month. However, wind speed distributions of low temporal resolution might not be able to account for high frequency variability which can lead to distributional shifts in the projected wind speeds. Even if these changes are small this can have a huge impact due to the highly non-linear relationship between wind and wind power and the long forecast horizons we consider. In my talk, I will discuss how the resolution of wind speed data from climate projections affects wind power forecasts.

## Multivariate forecasting in energy systems with a large share of renewables

Forecasts of renewable power production and electricity demand for multiple time periods and/or spatial expanses are required to operate modern power systems. Furthermore, probabilistic forecasts are necessary to facilitate economic decision-making and risk management. This gives rise to the challenge of producing forecasts that capture dependency between variables, over time, and between multiple locations. The Gaussian Copula has been widely used for multivariate energy forecasting, including for wind power, and is readily scalable given that the entire dependency structure is described by a single covariance matrix; however, estimating this covariance matrix in high dimensional problems remains a research challenge. Furthermore, it has been found empirically that this covariance matrix is often non-stationary and evolves over time. Two methods are presented for parameterising covariance matrices to enable conditioning on explanatory variables and as a step towards more robust estimation.

We consider two approaches, one based on modelling the parameters of covariance functions using additive models, and the second modelling individual elements of the modified Cholesky decomposition, again using additive models. We show how this gives rise to a wide range of possible parametric structures and discuss model selection and estimation strategies. Finally, we demonstrate though two case studies the improvement in forecast quality that these methods yield, and the importance and value of capturing the dynamics of dependency structures in wind power forecasting and net-load forecasting in the presence of embedded renewables.

## A Concise Overview on State-of-the-Art Solar Resources and Forecasting

The ability to forecast solar irradiance plays an indispensable role in solar power forecasting, which constitutes an essential step in planning and operating power systems under high penetration of solar power generation. Since solar radiation is an atmospheric process, solar irradiance forecasting, and thus solar power forecasting, can benefit from the participation of atmospheric scientists. In this talk, the two fields, namely, atmospheric science and power system engineering are jointly discussed with respect to how solar forecasting plays a part. Firstly, the state of affairs in solar forecasting is elaborated; some common misconceptions are clarified; and salient features of solar irradiance are explained. Next, five technical aspects of solar forecasting: (1) base forecasting methods, (2) post-processing, (3) irradiance-to-power conversion, (4) verification, and (5) grid-side implications, are reviewed. Following that, ten research topics moving into the future are enumerated; they are related to (1) data and tools, (2) numerical weather prediction, (3) forecast downscaling, (4) large eddy simulation, (5) dimming and brightening, (6) aerosols, (7) spatial forecast verification, (8) multivariate probabilistic forecast verification, (9) predictability, and (10) extreme weather events. Last but not least, a pathway towards ultra-high PV penetration is laid out, based on a recently proposed concept of firm generation and forecasting.

## Free boundary problems in optimal transportation

In this talk, we introduce some recent regularity results of free boundary in

optimal transportation. Particularly for higher order regularity, when

densities are Hölder continuous and domains are $C^2$, uniformly convex, we obtain the free boundary is $C^{2,\alpha}$ smooth. We also consider another mode

case that the target consists of two disjoint convex sets, in which

singularities of optimal transport mapping arise. Under similar assumptions,

we show that the singular set of the optimal mapping is an $(n-1)$-dimensional

$C^{2,\alpha}$ regular sub-manifold of $\mathbb{R}^n$. These are based on a

series of joint work with Shibing Chen and Xu-Jia Wang.

## A Nonsmooth Approach to Einstein's Theory of Gravity

While Einstein’s theory of gravity is formulated in a smooth setting, the celebrated singularity theorems of Hawking and Penrose describe many physical situations in which this smoothness must eventually breakdown. In positive-definite signature, there is a highly successful theory of metric and metric-measure geometry which includes Riemannian manifolds as a special case, but permits the extraction of nonsmooth limits under curvature and dimension bounds analogous to the energy conditions in relativity: here sectional curvature is reformulated through triangle comparison, while Ricci curvature is reformulated using entropic convexity along geodesics of probability measures.

This lecture explores recent progress in the development of an analogous theory in Lorentzian signature, whose ultimate goal is to provide a nonsmooth theory of gravity. We highlight how the null energy condition of Penrose admits a nonsmooth formulation as a variable lower bound on timelike Ricci curvature.

## Paths and Pathways

We talk about how some simple sounding problems about straight line paths on surfaces require many different kinds of mathematical thinking to solve, focusing on the example of understanding straight line paths on Platonic solids. We'll use this to start a discussion of how we can emphasize teaching different ways of thinking, and why geometry is an important resource for students. There will be lots and lots of fun pictures and hopefully interesting and provocative ideas!

The following resources are referenced during this talk:

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## Linearised Optimal Transport Distances

Optimal transport is a powerful tool for measuring the distances between signals and images. A common choice is to use the Wasserstein distance where one is required to treat the signal as a probability measure. This places restrictive conditions on the signals and although ad-hoc renormalisation can be applied to sets of unnormalised measures this can often dampen features of the signal. The second disadvantage is that despite recent advances, computing optimal transport distances for large sets is still difficult. In this talk I will extend the linearisation of optimal transport distances to the Hellinger–Kantorovich distance, which can be applied between any pair of non-negative measures, and the TLp distance, a version of optimal transport applicable to functions. Linearisation provides an embedding into a Euclidean space where the Euclidean distance in the embedded space is approximately the optimal transport distance in the original space. This method, in particular, allows for the application of off-the-shelf data analysis tools such as principal component analysis as well as reducing the number of optimal transport calculations from $O(n^2)$ to $O(n)$ in a data set of size n.