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A statistical model for the prediction of wind-speed probabilities in the atmospheric surface layer
Wind fields in the atmospheric surface layer (ASL) are highly three-dimensional and characterized by strong spatial and temporal variability. For various applications such as wind comfort assessments and structural design, an understanding of potentially hazardous wind extremes is important. Statistical models are designed to facilitate conclusions about the occurrence probability of wind speeds based on the knowledge of low-order flow statistics. Being particularly interested in the upper tail regions we show that the statistical behavior of near-surface wind speeds is adequately represented by the Beta distribution. By using the properties of the Beta probability density function in combination with a model for estimating extreme values based on readily available turbulence statistics, it is demonstrated that this novel modelling approach reliably predicts the upper margins of encountered wind speeds. The model’s basic parameter is derived from three substantially different calibrating datasets of flow in the ASL originating from boundary-layer wind-tunnel measurements and direct numerical simulation. Evaluating the model based on independent field observations of near-surface wind speeds showed a high level of agreement between the statistically modelled horizontal wind speeds and measurements. The results show that, based on the knowledge of only a few simple flow statistics (mean wind speed, wind speed fluctuations and integral time scales), the occurrence probability of velocity magnitudes at arbitrary flow locations in the ASL can be estimated with a high degree of confidence
A Review of Classification Problems and Algorithms in Renewable Energy Applications
Classification problems and their corresponding solving approaches constitute one of the
fields of machine learning. The application of classification schemes in Renewable Energy (RE) has
gained significant attention in the last few years, contributing to the deployment, management and
optimization of RE systems. The main objective of this paper is to review the most important
classification algorithms applied to RE problems, including both classical and novel algorithms.
The paper also provides a comprehensive literature review and discussion on different classification
techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in
RE systems, power quality disturbance classification and other applications in alternative RE systems.
In this way, the paper describes classification techniques and metrics applied to RE problems,
thus being useful both for researchers dealing with this kind of problem and for practitioners
of the field
Current gust forecasting techniques, developments and challenges
Gusts represent the component of wind most likely to be associated with
serious hazards and structural damage, representing short-lived extremes
within the spectrum of wind variation. Of interest both for short range
forecasting and for climatological and risk studies, this is also reflected
in the variety of methods used to predict gusts based on various static and
dynamical factors of the landscape and atmosphere. The evolution of Numerical
Weather Prediction (NWP) models has delivered huge benefits from increasingly
accurate forecasts of mean near-surface wind, with which gusts broadly scale.
Techniques for forecasting gusts rely on parametrizations based on a physical
understanding of boundary layer turbulence, applied to NWP model fields, or
statistical models and machine learning approaches trained using
observations, each of which brings advantages and disadvantages.Major shifts in the nature of the information available from NWP models are underway with the advent of
ever-finer resolution and ensembles increasingly employed at the regional scale. Increases in the resolution of
operational NWP models mean that phenomena traditionally posing a challenge for gust forecasting, such as
convective cells, sting jets and mountain lee waves may now be at least partially represented in the model fields.
This advance brings with it significant new questions and challenges, such as concerning: the ability of traditional gust prediction
formulations to continue to perform as phenomena associated with gusty conditions become increasingly resolved; the extent to
which differences in the behaviour of turbulence associated with each phenomenon need to be accommodated in future gust prediction
methods. A similar challenge emerges from the increasing, but still partial resolution of terrain detail in NWP
models; the speed-up of the mean wind over resolved hill tops may be realistic, but may have negative impacts
on the performance of gust forecasting using current methods. The transition to probabilistic prediction using
ensembles at the regional level means that considerations such as these must also be carried through to the
aggregation and post-processing of ensemble members to produce the final forecast. These issues and their implications are discussed.</p