795 citations · 814 across the 11 of their papers we have counts for
11 papers
Domain-Adapted Power Curve for Cross-Farm Applications
Ahmadreza Chokhachian, V. Roshan Joseph, Yu Ding
The wind energy industry relies on accurate power curve models to make power forecast, evaluate turbine performance, quantify upgrade, or support site-planning decisions. In this p…
Spatio-Temporal Gaussian Process for Building Terrain-Incorporating Wind Power Curves
Ahmadreza Chokhachian, V. Roshan Joseph, Yu Ding
Accurate modeling of wind turbine power curves is crucial for optimal wind farm operation. Nearly all existing power curve models focus on temporal variables such as wind speed and…
Factor Importance Ranking and Selection using Total Indices
Chaofan Huang, V. Roshan Joseph
Factor importance measures the impact of each feature on output prediction accuracy. Many existing works focus on the model-based importance, but an important feature in one learni…
Rational Kriging
V. Roshan Joseph
This article proposes a new kriging that has a rational form. It is shown that the generalized least squares estimate of the mean from rational kriging is much more well behaved th…
Discovering the Unknowns: A First Step
V. Roshan Joseph, William E. Lewis, Henry S. Yuchi +1
This article aims at discovering the unknown variables in the system through data analysis. The main idea is to use the time of data collection as a surrogate variable and try to i…
Enhancing Sample Quality through Minimum Energy Importance Weights
Chaofan Huang, V. Roshan Joseph
Importance sampling is a powerful tool for correcting the distributional mismatch in many statistical and machine learning problems, but in practice its performance is limited by t…
Asset Bundling for Wind Power Forecasting
Hanyu Zhang, Mathieu Tanneau, Chaofan Huang +3
The growing penetration of intermittent, renewable generation in US power grids, especially wind and solar generation, results in increased operational uncertainty. In that context…
A Global-Local Approximation Framework for Large-Scale Gaussian Process Modeling
Akhil Vakayil, Roshan Joseph
In this work, we propose a novel framework for large-scale Gaussian process (GP) modeling. Contrary to the global, and local approximations proposed in the literature to address th…
Optimal Ratio for Data Splitting
V. Roshan Joseph
It is common to split a dataset into training and testing sets before fitting a statistical or machine learning model. However, there is no clear guidance on how much data should b…
Constrained Minimum Energy Designs
Chaofan Huang, V. Roshan Joseph, Douglas M. Ray
Space-filling designs are important in computer experiments, which are critical for building a cheap surrogate model that adequately approximates an expensive computer code. Many d…
Population Quasi-Monte Carlo
Chaofan Huang, V. Roshan Joseph, Simon Mak
Monte Carlo methods are widely used for approximating complicated, multidimensional integrals for Bayesian inference. Population Monte Carlo (PMC) is an important class of Monte Ca…