With the increase in demand for energy, penetration of alternative sources of energy in the
power grid has increased. Photovoltaic (PV) energy is the most common and popular form of …

In this article, we provide a comprehensive review of space–time covariance functions. As
for the spatial domain, we focus on either the d‐dimensional Euclidean space or on the unit …

降雨量可通过空间相关的随机场叠加在均值背景上进行建模, 因而地统计方法适合于雨量台站
数据的分析. 然而要形成有用的结果, 则必须考虑到这些数据的某些典型特征, 主要包括 …

This work considers a number of properties of space–time covariance functions and how
these relate to the spatial-temporal interactions of the process. First, it examines how the …

Environmental and geophysical processes such as atmospheric pollutant concentrations,
precipitation fields and surface winds are characterized by spatial and temporal variability. In …

In the spatial or spatio-temporal context, specifying the correct covariance function is
fundamental to obtain efficient predictions, and to understand the underlying physical …

With the global proliferation of wind power, the need for accurate short-term forecasts of
wind resources at wind energy sites is becoming paramount. Regime-switching space–time …

Spatio-temporal statistical models are increasingly being used across a wide variety of
scientific disciplines to describe and predict spatially-explicit processes that evolve over …

The emphasis on renewable energy and concerns about the environment have led to large‐
scale wind energy penetration worldwide. However, there are also significant challenges …

In this article, we propose two methods for estimating space and space-time covariance
functions from a Gaussian random field, based on the composite likelihood idea. The first …