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

Autor:	Li, Qiqi, Ludkovski, Mike
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Electrical Engineering and Systems Science - Systems and Control Physics - Atmospheric and Oceanic Physics Physics - Data Analysis Statistics and Probability Statistics - Applications
Druh dokumentu:	Working Paper
Popis:	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. Comment: 29 pages, 12 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.16308 Zobrazit plný text záznamu View this record from Arxiv