In this article we present an approach that enables joint wind speed and wind power forecasts for a wind park. We combine a multivariate seasonal time varying threshold autoregressive moving average (TVARMA) model with a power threshold generalized autoregressive conditional heteroscedastic (power-TGARCH) model. The modeling framework incorporates diurnal and annual periodicity modeling by peri...

Light regimes vary significantly within small forest openings, rangingfrom full sunlight to total shade, and they may affect the establishment and early growth of oak seedlings. We designed modified shadehouses to simulate the complex light conditions within forest openings and tested the effects of daily photosynthetically active radiation (PAR), time of direct light exposure, and the ratio of...

The acoustical signal generated by a helicopter, as well as many other signals, e.g., a voiced speech signal, can be described as periodic or almost periodic. When these signals are observed in the presence of other additive wide-band signals, it becomes interesting to separate these two kinds of signals. In this paper we specifically present an iterative method for separating helicopter signal...

Each D A microarray experiment generates a large amount of gene expression profiles and it remains a challenge for biologists to robustly identify periodic gene expression profiles with certain noise level in the data. In this paper, we propose a new scheme with noise filtering technique to analyze the periodicity of gene expression base on singular value decomposition (SVD), singular spectrum ...

In electrical power systems with strong hydro generation, the use of adequate techniques to generate synthetic hydrological scenarios is extremely important for the evaluation of the ways the system behaves in order to meet the forecast energy demand. This paper proposes a new model to generate natural inflow energy scenarios in the long-term operation planning of large-sized hydrothermal syste...

In dynamic data driven applications modeling accurately the uncertainty of various inputs is a key step of the process. In this paper, we first review the basics of the Karhunen-Loève decomposition as a means for representing stochastic inputs. Then, we derive explicit expressions of one-dimensional covariance kernels associated with periodic spatial second-order autoregressive processes. We al...