Advancing Wind Energy Forecasts with Continuous Generative Representations

By Xihaier Luo and Byung-Jun Yoon

Climate change is a critical issue that requires immediate attention. Rising global temperatures and the prospect of further warming pose significant threats to ecosystems, economies, and societies around the world. A rapid shift to renewable energy sources would help to mitigate global warming, and wind power stands out as an abundant, widely distributed, and emission-free resource. However, the optimization of wind energy usage faces challenges pertaining to wind data resolution. Our work explores the potential of continuous generative representations for improving wind data resolution and enhancing renewable energy forecasts.

Accurate wind data resolution is essential for assessing and optimizing renewable energy resources. Though researchers often use numerical weather prediction models to generate wind data, these models currently operate at fixed resolutions of approximately 100 kilometers (km) — an inadequate level for precise analysis. Wind resource assessments ideally require resolutions that are finer than 10 km (and preferably closer to two km). Insufficient resolution hampers the development of renewable energy farms and limits their long-term economic sustainability. To overcome the limitations of discrete climate data, we propose an investigation to recover the intrinsic continuous climate patterns from such data.

Figure 1. The proposed implicit neural network model. Figure courtesy of Xihaier Luo.

We use a weather and climate dataset to illustrate the basic idea. One can think of a geophysical variable as a scalar function of physical variables like latitude, longitude, and time. Because the underlying field generation process frequently lacks a well-defined analytic form, scientists handcraft and optimize parameters \(\Theta\) to describe the functions. We refer to such a field as \(\mathbf{v}=F_\Theta(\mathbf{s})\), where \(\mathbf{v}\) denotes the geophysical variable (in this case, the wind speed), \(\mathbf{s}\) indicates the spatial coordinate, and \(F(\cdot)\) represents a numerical weather model. Climate data typically comprise index sampled functions with discrete values (such as camera pixels), or discrete function parameterizations with voxels or discretized level sets. Accordingly, researchers commonly define the super-resolution task as 

\[\mathbf{v}_\textrm{high}=\mathcal{M}(\mathbf{v}_\textrm{low}).\tag1\]

Ideally, we want to access the climate property not just at fixed discrete locations, but at every possible point \(\mathbf{s} \in \mathbb{R}^2\). Furthermore, we argue that the super-resolution formulation in \((1)\) ignores the inherent physics that are encapsulated in \(F(\cdot)\). As an alternative approach, we compress the low-resolution data by training a neural network \(\hat{F}(\cdot)\) to capture the desired climate property as a continuous scalar function of space coordinates:

\[\mathbf{v}=\hat{F}(\mathbf{s}).\tag2\]

After training the model such that \(\hat{F} \approx F\), we can use it to make inferences for any continuous space coordinate in the domain and generate \(\mathbf{v}_\textrm{high}\) at any resolution.

Figure 2. Qualitative illustration of learning continuous representation. We assess the model performance at various upsampling scales. Figure courtesy of Xihaier Luo.

Our research proposes an innovative coordinate-based deep learning model to address the challenge of continuous super-resolution for climate data. Specifically, we have focused on developing an implicit neural network model that is designed to learn continuous representations of climate data (see Figures 1 and 2). However, our work extends beyond this achievement. Modern numerical weather and climate simulations—which can reach hundreds of terabytes to several petabytes—generate staggering volumes of data. As the demand intensifies for higher-resolution simulations to tackle climate change and associated extreme weather events, the size of these datasets will continue to escalate. In light of this persistent increase, we plan to also create a model that is capable of effectively compressing multidimensional weather and climate data.

Acknowledgments: The work presented in the article is supported by funding from the Advanced Scientific Computing Research program in the Department of Energy’s Office of Science under project B&R #KJ0402010.

Xihaier Luo is an assistant computational scientist in the Computational Science Initiative at Brookhaven National Laboratory. He develops scientific machine learning models that enable the prediction, optimization, and uncertainty quantification of complex and large-scale physical systems.

Byung-Jun Yoon is an associate professor in the Department of Electrical and Computer Engineering at Texas A&M University. He also holds a joint appointment at Brookhaven National Laboratory. Yoon works on the development of machine learning methods for large-scale physical systems and their subsequent application to various scientific domains, including computational biology and materials science.