2017/06/14 by Emil B. Iversen, Rune Juhl, Iversen, Emil B. +10
Agricultural and Biological Sciences · Engineering · Environmental Science · Mathematics · #Agricultural Economics and Policy #Applications (stat.AP) #Climate change impacts on agriculture #Climate variability and models #FOS: Computer and information sciences #Plant Water Relations and Carbon Dynamics #Remote Sensing in Agriculture #Water resources management and optimization #stat.AP
paper · pdf · doi:10.48550/arxiv.1706.04394
24 pages, 6 figures
arxiv created 2017/06/14 · openalex publication_date 2017/06/14 · arxiv updated 2017/06/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Spatio-temporal problems exist in many areas of knowledge and disciplines ranging from biology to engineering and physics. However, solution strategies based on classical statistical techniques often fall short due to the large number of parameters that are to be estimated and the huge amount of data that need to be handled. In this paper we apply known techniques in a novel way to provide a framework for spatio-temporal modeling which is both computationally efficient and has a low dimensional parameter space. We present a micro-to-macro approach whereby the local dynamics are first modeled and subsequently combined to capture the global system behavior. The proposed methodology relies on coupled stochastic differential equations and is applied to produce spatio-temporal forecasts for a solar power plant for very short horizons, which essentially implies tracking clouds moving across the field of solar power inverters. We outperform simple and complex benchmarks while providing forecasts for 70 spatial dimensions and 24 lead times (i.e., for a total number of random variables equal to 1680). The resulting model can provide all sorts of forecast products, ranging from point forecasts and co-variances to predictive densities, multi-horizon forecasts, and space-time trajectories.