Probabilistic Deep Learning for Wind Turbines

Model speed can be a deal breaker on large datasets. Leveraging an empirical study, we will look at two dimension reduction techniques and how they can be applied to a Gaussian Processes. Regarding implementation of the method, anyone familiar with the basics of conditional probability can develop a Gaussian Process model. However, to fully leverage the capabilities of the framework, a fair amount of in-depth knowledge is required. Gaussian processes also are not very computationally efficient, but their flexibility is makes them a common choice for niche regression problems.