Probabilistic wind power forecasts with an inverse power curve transformation and censored regression

Forecasting wind power is an important part of a successful integration of wind power into the power grid. Forecasts with lead times longer than 6 h are generally made by using statistical methods to post-process forecasts from numerical weather prediction systems. Two major problems that complicate this approach are the non-linear relationship between wind speed and power production and the limited range of power production between zero and nominal power of the turbine. In practice, these problems are often tackled by using non-linear non-parametric regression models. However, such an approach ignores valuable and readily available information: the power curve of the turbine's manufacturer. Much of the non-linearity can be directly accounted for by transforming the observed power production into wind speed via the inverse power curve so that simpler linear regression models can be used. Furthermore, the fact that the transformed power production has a limited range can be taken care of by employing censored regression models. In this study, we evaluate quantile forecasts from a range of methods: (i) using parametric and non-parametric models, (ii) with and without the proposed inverse power curve transformation and (iii) with and without censoring. The results show that with our inverse (power-to-wind) transformation, simpler linear regression models with censoring perform equally or better than non-linear models with or without the frequently used wind-to-power transformation.

Identification of stimulatory and inhibitory inputs to the hypothalamic-pituitary-adrenal axis during hypoglycaemia or transport in ewes

This study used the novel approach of statistical modelling to investigate the control of hypothalamic-pituitary-adrenal (HPA) axis and quantify temporal relationships between hormones. Two experimental paradigms were chosen, insulin-induced hypoglycaemia and 2 h transport, to assess differences in control between noncognitive and cognitive stimuli. Vasopressin and corticotropin-releasing hormone (CRH) were measured in hypophysial portal plasma, and adrenocorticotropin hormone (ACTH) and cortisol in jugular plasma of conscious sheep, and deconvolution analysis was used to calculate secretory rates, before modelling. During hypoglycaemia, the relationship between plasma glucose and vasopressin or CRH was best described by log(10) transforming variables (i.e. a positive power-curve relationship). A negative-feedback relationship with log(10) cortisol concentration 2 h previously was detected. Analysis of the 'transport' stimulus suggested that the strength of the perceived stimulus decreased over time after accounting for cortisol facilitation and negative-feedback. The time course of vasopressin and CRH responses to each stimulus were different However, at the pituitary level, the data suggested that log(10) ACTH secretion rate was related to log(10) vasopressin and CRH concentrations with very similar regression coefficients and an identical ratio of actions (2.3 : 1) for both stimuli. Similar magnitude negative-feedback effects of log(10) cortisol at -110 min (hypoglycaemia) or -40 min (transport) were detected, and both models contained a stimulatory relationship with cortisol at 0 min (facilitation). At adrenal gland level, cortisol secretory rates were related to simultaneously measured untransformed ACTH concentration but the regression coefficient for the hypoglycaemia model was 2.5-fold greater than for transport. No individual sustained maximum cortisol secretion for longer than 20 min during hypoglycaemia and 40 min during transport. These unique models demonstrate that corticosteroid negative-feedback is a significant control mechanism at both the pituitary and hypothalamus. The amplitude of HPA response may be related to stimulus intensity and corticosteroid negative-feedback, while duration depended on feedback alone.

A Wiener model for memory high power amplifiers using B-spline function approximation

In this paper we introduce a new Wiener system modeling approach for memory high power amplifiers in communication systems using observational input/output data. By assuming that the nonlinearity in the Wiener model is mainly dependent on the input signal amplitude, the complex valued nonlinear static function is represented by two real valued B-spline curves, one for the amplitude distortion and another for the phase shift, respectively. The Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first order derivatives recursion. An illustrative example is utilized to demonstrate the efficacy of the proposed approach.

Curve crossing for the reflected Levy process at zero and infinity

Let $R_{t}=\sup_{0\leq s\leq t}X_{s}-X_{t}$ be a Levy process reflected in its maximum. We give necessary and sufficient conditions for finiteness of passage times above power law boundaries at infinity. Information as to when the expected passage time for $R_{t}$ is finite, is given. We also discuss the almost sure finiteness of $\limsup_{t\to 0}R_{t}/t^{\kappa}$, for each $\kappa\geq 0$.