Abstract. The impact of climate change on water resources is usually assessed at the local scale. However, regional climate models (RCMs) are known to exhibit systematic biases in precipitation. Hence, RCM simulations need to be post-processed in order to produce reliable estimates of local scale climate. Popular post-processing approaches are based on statistical transformations, which attempt to adjust the distribution of modelled data such that it closely resembles the observed climatology. However, the diversity of suggested methods renders the selection of optimal techniques difficult and therefore there is a need for clarification. In this paper, statistical transformations for postprocessing RCM output are reviewed and classified into (1) distribution derived transformations, (2) parametric transformations and (3) nonparametric transformations, each differing with respect to their underlying assumptions. A real world application, using observations of 82 precipitation stations in Norway, showed that nonparametric transformations have the highest skill in systematically reducing biases in RCM precipitation.

Wind power forecasts are in various ways valuable for users in decision‐making processes. However, most forecasts are deterministic, and hence possibly important information about uncertainty is not available. Complete information about future production can be obtained by using probabilistic forecasts, and this article demonstrates how such forecasts can be created by means of local quantile regression. The approach has several advantages, such as no distributional assumptions and flexible inclusion of predictive information. In addition, it can be shown that, for some purposes, forecasts in terms of quantiles provide the type of information required to make optimal economic decisions. The methodology is applied to data from a wind farm in Norway. Copyright © 2004 John Wiley & Sons, Ltd.

CapsuleState-of-the-Art statistical postprocessing techniques for ensemble forecasts are reviewed, together with the challenges posed by a demand for timely, high-resolution and reliable probabilistic information. Possible research avenues are also discussed.

International audienceThis paper presents an overview of the state of the art on the research on Dynamic Line Rating forecasting. It is directed at researchers and decision-makers in the renewable energy and smart grids domain, and in particular at members of both the power system and meteorological community. Its aim is to explain the details of one aspect of the complex interconnection between the environment and power systems. The ampacity of a conductor is defined as the maximum constant current which will meet the design, security and safety criteria of a particular line on which the conductor is used. Dynamic Line Rating (DLR) is a technology used to dynamically increase the ampacity of electric overhead transmission lines. It is based on the observation that the ampacity of an overhead line is determined by its ability to dissipate into the environment the heat produced by Joule effect. This in turn is dependent on environmental conditions such as the value of ambient temperature, solar radiation, and wind speed and direction. Currently, conservative static seasonal estimations of meteorological values are used to determine ampacity. In a DLR framework, the ampacity is estimated in real time or quasi-real time using sensors on the line that measure conductor temperature, tension, sag or environmental parameters such as wind speed and air temperature. Because of the conservative assumptions used to calculate static seasonal ampacity limits and the variability of weather parameters, DLRs are considerably higher than static seasonal ratings. The latent transmission capacity made available by DLRs means the operation time of equipment can be extended, especially in the current power system scenario, where power injections from Intermittent Renewable Sources (IRS) put stress on the existing infrastructure. DLR can represent a solution for accommodating higher renewable production whilst minimizing or postponing network reinforcements. On the other hand, the variability of DLR with respect to static seasonal ratings makes it particularly difficult to exploit, which explains the slow take-up rate of this technology. In order to facilitate the integration of DLR into power system operations, research has been launched into DLR forecasting, following a similar avenue to IRS production forecasting, i.e. based on a mix of statistical methods and meteorological forecasts. The development of reliable DLR forecasts will no doubt be seen as a necessary step for integrating DLR into power system management and reaping the expected benefits

During the last few years, probabilistic wind power forecasts have received increasing attention because of their assumed value in decision‐making processes. In the current article, three statistical methods are described and several models based on these are compared. The statistical methods are local quantile regression, a local Gaussian model and the Nadaraya–Watson estimator for conditional cumulative distribution functions. The focus is on quantile forecasts, since these often provide the required type of information to make optimal economic decisions and are ideal for visualizing uncertainty. The statistical methods are applied to data from a wind farm in Norway and results are compared using appropriate measures for assessment of quantile forecasts and in terms of a simple model for economic value. Copyright © 2005 John Wiley & Sons, Ltd.