Spatio-Temporal Gaussian Process for Building Terrain-Incorporating Wind Power Curves

2026-06-29

AI Summary

This study addresses the limitation of conventional wind turbine power curve models, which typically neglect terrain effects and thus struggle to accurately represent wind energy production in complex terrains. To overcome this, the authors propose a nonparametric spatiotemporal Gaussian process model that systematically incorporates terrain covariates for the first time. The model introduces a shared set of representative temporal covariates to handle temporal misalignment across multiple turbines and employs a separable kernel structure to efficiently capture spatiotemporal dependencies while reducing computational complexity. Experimental results on real-world wind farm data demonstrate that the proposed approach significantly outperforms existing baselines, achieving higher prediction accuracy and enabling quantitative assessment of how distinct terrain features influence turbine performance.

Abstract

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 temperature while overlooking the influence of terrain covariates, which governs inflow wind conditions and thus also affects wind power production. This paper proposes a nonparametric spatio-temporal Gaussian process model that integrates temporal environmental covariates with spatial terrain features. The model falls in the category of spatial-temporal Gaussian process models with data on a grid. The challenge to be addressed is that the spatio-temporal modeling require certain temporal alignment among the data, a property that the wind farm data does not have. Our solution strategy is to construct a shared representative temporal covariate set which not only aligns the temporal inputs but also has a size an order of magnitude smaller than the original data size. With this transformation, our resulting model is able to employ a separable kernel structure that captures both spatial and temporal dependencies. Empirical analysis on a real wind farm dataset shows that our method improves predictive accuracy over existing baselines and can be used to quantify the various impact of the terrain characteristics on turbine performance.

Problem

• wind power curve
• terrain covariates
• spatio-temporal modeling
• temporal alignment
• Gaussian process

Innovation

• spatio-temporal Gaussian process
• terrain-incorporating power curve
• wind farm modeling
• temporal alignment
• separable kernel