# Probabilistic Spatiotemporal Modeling of Day-Ahead Wind Power Generation with Input-Warped Gaussian Processes

arXiv: https://arxiv.org/abs/2409.16308 · HTML: https://mlsanity.com/paper/2409.16308/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 58 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Input Warping for Bayesian Optimization of Non-stationary Functions](https://mlsanity.com/paper/1402.0929/) (2014)
- [A general framework for Vecchia approximations of Gaussian processes](https://mlsanity.com/paper/1708.06302/) (2017)
- [A Case Study Competition Among Methods for Analyzing Large Spatial Data](https://mlsanity.com/paper/1710.05013/) (2017)
- [Deep Compositional Spatial Models](https://mlsanity.com/paper/1906.02840/) (2019)
- [Basis-Function Models in Spatial Statistics](https://mlsanity.com/paper/2202.03660/) (2022)
- [MOGPTK: The Multi-Output Gaussian Process Toolkit](https://mlsanity.com/paper/2002.03471/) (2020)
- [Gaussian Process Regression for Probabilistic Short-term Solar Output Forecast](https://mlsanity.com/paper/2002.10878/) (2020)
- [Constructing Large Nonstationary Spatio-Temporal Covariance Models via Compositional Warpings](https://mlsanity.com/paper/2202.03560/) (2022)
- [Extreme Scenario Selection in Day-Ahead Power Grid Operational Planning](https://mlsanity.com/paper/2309.11067/) (2023)
- [Cost Attribution And Risk-Averse Unit Commitment In Power Grids Using Integrated Gradient](https://mlsanity.com/paper/2408.04830/) (2024)

## Built on by (most cited first, 1 of 1)

- [Selecting Critical Scenarios of DER Adoption in Distribution Grids Using Bayesian Optimization](https://mlsanity.com/paper/2501.14118/) (2025)

## Abstract (arXiv)

We design a Gaussian Process (GP) spatiotemporal model to capture features of day-ahead wind power forecasts. We work with hourly-scale day-ahead forecasts across hundreds of wind farm locations, with the main aim of constructing a fully probabilistic joint model across space and hours of the day. To this end, we design a separable space-time kernel, implementing both temporal and spatial input warping to capture the non-stationarity in the covariance of wind power. We conduct synthetic experiments to validate our choice of the spatial kernel and to demonstrate the effectiveness of warping in addressing nonstationarity. The second half of the paper is devoted to a detailed case study using a realistic, fully calibrated dataset representing wind farms in the ERCOT region of Texas.