Summary of Probabilistic Spatiotemporal Modeling Of Day-ahead Wind Power Generation with Input-warped Gaussian Processes, by Qiqi Li and Mike Ludkovski
Probabilistic Spatiotemporal Modeling of Day-Ahead Wind Power Generation with Input-Warped Gaussian Processes
by Qiqi Li, Mike Ludkovski
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Atmospheric and Oceanic Physics (physics.ao-ph); Data Analysis, Statistics and Probability (physics.data-an); Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary We present a Gaussian Process (GP) model for day-ahead wind power forecasts, focusing on constructing a probabilistic joint model across space and time. Our design features a separable space-time kernel that incorporates temporal and spatial input warping to capture non-stationarity in the covariance of wind power. Synthetic experiments validate our choice of spatial kernel and demonstrate the effectiveness of warping in addressing nonstationarity. We then apply this approach to a realistic, fully calibrated dataset representing wind farms in Texas’s ERCOT region. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about predicting how much wind power will be generated at different locations over the next day. To do this, we use a special kind of model called a Gaussian Process (GP) that can handle data from many places and times. We also come up with a way to make our predictions better by “warping” the time and space to match how wind power actually behaves. We test our idea using fake data and then apply it to real data about wind farms in Texas. |
