arXiv:2607.00051v1 Announce Type: cross 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 pro
The paper leverages advanced spatio-temporal Gaussian process models, which have matured sufficiently to integrate complex environmental and topographical data for more precise wind energy predictions.
Improved wind power curve modeling, by integrating terrain data, leads to more efficient wind farm operation and better integration of renewable energy into grids, impacting overall energy stability and costs.
Wind power forecasting and grid management can become more accurate and reliable, allowing for better resource utilization and potentially reducing the intermittency challenges of wind energy.
- · Wind farm operators
- · Renewable energy sector
- · Energy grid operators
- · Computational modeling specialists
- · Inefficient wind energy forecasting methods
- · Fossil fuel-dependent energy producers (marginally)
More precise wind power generation forecasts become available for operational planning.
Enhanced predictability contributes to a more stable and cost-effective renewable energy supply, accelerating adoption.
Increased reliability of wind energy may influence energy policy decisions and investment towards larger-scale wind developments.
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Read at arXiv cs.LG