Related papers: A simple method to downscale daily wind statistics…
Accurate real-time wind vector estimation is essential for enhancing the safety, navigation accuracy, and energy efficiency of unmanned aerial vehicles (UAVs). Traditional approaches rely on external sensors or simplify vehicle dynamics,…
The widespread utilisation of grid-integrated wind electricity necessitates accurate and reliable wind speed forecasting to ensure stable grid and quality power. Machine learning algorithm based wind speed forecasting models are getting…
The solar wind influence on geospace can be described as the sum of a directly driven component, or dayside reconnection, and an unloading component, associated with the release of magnetic energy via nightside reconnection. The two…
An extreme wind speed estimation method that considers wind hazard climate types is critical for design wind load calculation for building structures affected by mixed climates. However, it is very difficult to obtain wind hazard climate…
Winds play an essential role in the climate system. In this study, we analyze the global pattern of the diurnal cycle of surface (10 m) winds from the ERA5 reanalysis data. We find that over the land and especially over sand dune regions,…
Wind farms can be regarded as complex systems that are, on the one hand, coupled to the nonlinear, stochastic characteristics of weather and, on the other hand, strongly influenced by supervisory control mechanisms. One crucial problem in…
Current wind turbine simulations successfully use turbulence generating tools for modeling behavior. However, they lack the ability to reproduce variabilities in wind dynamics and inherent stochastic structures (like temporal and spatial…
Wind speed retrieval at sea surface is of primary importance for scientific and operational applications. Besides weather models, in-situ measurements and remote sensing technologies, especially satellite sensors, provide complementary…
Probabilistic forecasts of wind speed are important for a wide range of applications, ranging from operational decision making in connection with wind power generation to storm warnings, ship routing and aviation. We present a statistical…
This work explores the relationship between wind speed and time-dependent structural motion response as a means of leveraging the rich information visible in flow-structure interactions for anemometry. We build on recent work by Cardona et…
The growth of wind generation capacities in the past decades has shown that wind energy can contribute to the energy transition in many parts of the world. Being highly variable and complex to model, the quantification of the…
The ability to predict wind is crucial for both energy production and weather forecasting. Mechanistic models that form the basis of traditional forecasting perform poorly near the ground. In this paper, we take an alternative data-driven…
Machine learning models have been employed to perform either physics-free data-driven or hybrid dynamical downscaling of climate data. Most of these implementations operate over relatively small downscaling factors because of the challenge…
Accurate, efficient prediction of wind flow with wake effects is crucial for wind-farm layout and power forecasting. Existing approaches-physical measurements, numerical simulations, physics-based models, and data-driven models-face…
Accurately estimating latent velocity vector fields of atmospheric winds is crucial for understanding weather phenomena. Direct measurement of atmospheric winds is costly, especially in the upper atmosphere, so researchers attempt to…
World is looking for clean and renewable energy sources that do not pollute the environment, in an attempt to reduce greenhouse gas emissions that contribute to global warming. Wind energy has significant potential to not only reduce…
This paper addresses the problem of predicting a wind farm's power generation when no or few statistical data is available. The study is based on a time-series wind speed model and on a simple dynamic model of a DFIG wind turbine including…
Modelling the intermittency of wind speed has got significant relevance on many fields, including wind power generation and distribution. Most of the available modelling techniques assume the temporal fluctuations in the wind is stochastic…
Accurate wind speed forecasting is of great importance for many economic, business and management sectors. This paper introduces a new model based on convolutional neural networks (CNNs) for wind speed prediction tasks. In particular, we…
A comprehensive statistical model for vertical profiles of the horizontal wind and temperature throughout the troposphere is presented. The model is based on radiosonde measurements of wind and temperature during several years. The profiles…