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Time-Series Embeddings from Language Models: A Tool for wind direction Nowcasting

Journal of Meteorological Research 2024年第3期38卷 558-569页

作者： Décio ALVES Fábio MENDON?A Sheikh Shanawaz MOSTAFA Fernando MORGADO-DIAS University of Madeira Campus Universitário da Penteada 9020-105 Funchal Interactive Technologies Institute/Laboratory for Robotics and Engineering Systems and Agência Regional para o Desenvolvimento da Investiga??o Tecnologia e Inova??o Edif.Madeira Tecnopolo

wind direction nowcasting is crucial in various sectors, particularly for ensuring aviation operations and safety. In this context, the TELMo(Time-series Embeddings from Language Models) model, a sophisticated deep learning architecture, has been introduced in this work for enhanced wind-direction nowcasting. Developed by using three years of data from multiple stations in the complex terrain of an international airport, TELMo incorporates the horizontal u(east–west) andv (north–south) wind components to significantly reduce forecasting errors. On a day with high wind direction variability, TELMo achieved mean absolute error values of 5.66 for 2-min, 10.59 for 10-min, and 14.79 for20-min forecasts, processed within a swift 9-ms/step timeframe. Standard degree-based analysis, in comparison, yielded lower performance, emphasizing the effectiveness of the u and v components. In contrast, a Vanilla neural network, representing a shallow-learning approach, underperformed in all analyses, highlighting the superiority of deep learning methodologies in wind direction nowcasting. TELMo is an efficient model, capable of accurately forecasting wind direction for air traffic operations, with an error less than 20° in 97.49% of the predictions, aligning with recommended international thresholds. This model design enables its applicability across various geographical locations, making it a versatile tool in global aviation meteorology.

关键词： wind nowcasting wind components wind direction time series prediction deep learning

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Multi-hazard joint probability distribution model for wind speed,wind direction and rain intensity

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Science China(Technological Sciences) 2023年第2期66卷 336-345页

作者： BI WenZhe TIAN Li LI Chao ZHANG SiYao School of Civil Engineering Shandong UniversityJinan 250061China Faculty of Infrastructure Engineering Dalian University of TechnologyDalian 116024China

Multiple disasters such as strong wind and torrential rain pose great threats to civil infrastructures.However,most existing studies ignored the dependence structure between them,as well as the effect of wind direction.From the dimension of the engineering sector,this paper introduces the vine copula to model the joint probability distribution(JPD)of wind speed,wind direction and rain intensity based on the field data in Yangjiang,China during 1971–2020.First,the profiles of wind and rain in the studied area are statistically analyzed,and the original rainfall amounts are converted into short-term rain intensity.Then,the marginal distributions of individual variables and their pairwise dependence structures are built,followed by the development of the trivariate joint distribution model.The results show that the constructed vine copula-based model can well characterize the dependence structure between wind speed,wind direction and rain intensity.Meanwhile,the JPD characteristics of wind speed and rain intensity show significant variations depending on wind direction,thus the effect of wind direction cannot be neglected.The proposed JPD model will be conducive for reasonable and precise performance assessment of structures subjected to multiple hazards of wind and rain actions.

关键词： joint probability distribution wind speed rain intensity wind direction vine copulas directionality effects

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Joint Model of wind Speed and Corresponding direction Based on wind Rose for wind Energy Exploitation

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Journal of Ocean University of China 2022年第4期21卷 876-892页

作者： YANG Zihao LIN Yifan DONG Sheng College of Engineering Ocean University of ChinaQingdao 266100China Huadian Heavy Industries Co. Ltd.Beijing 100070China

As a common and extensive datum to analyze wind,wind rose is one of the most important components of the meteorological elements.In this study,a model is proposed to establish the joint probability distribution of wind speed and direction using grouped data of wind rose.On the basis of the model,an algorithm is presented to generate pseudorandom numbers of wind speed and paired direction data.Afterward,the proposed model and algorithm are applied to two weather stations located in the Liaodong Gulf.With the models built for the two cases,a novel graph representing the continuous joint probability distribution of wind speed and direction is plotted,showing a strong correlation to the corresponding wind rose.Moreover,the joint probability distributions are utilized to evaluate wind energy potential successfully.In cooperation with Monte Carlo simulation,the model can approximately predict annual directional extreme wind speed under different return periods under the condition that the wind rose can represent the meteorological characters of the wind field well.The model is beneficial to design and install wind turbines.

关键词： angular-linear distribution wind speed wind direction wind rose wind energy Monte Carlo simulation

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Across two phylogeographic breaks: Quaternary evolutionary history of a mountain aspen (Populus rotundifolia) in the Hengduan Mountains

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Plant Diversity 2024年第3期46卷 321-332页

作者： Jieshi Tang Xiaoyan Fan Richard I.Milne Heng Yang Wenjing Tao Xinran Zhang Mengyun Guo Jialiang Li Kangshan Mao Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education College of Life SciencesState Key Laboratory of Hydraulics and Mountain River EngineeringSichuan UniversityChengdu 610065PR China Institute of Molecular Plant Sciences University of EdinburghEdinburgh EH93JHUK School of Ecology and Environment Tibet UniversityLhasa 850000PR China

Biogeographical barriers to gene flow are central to plant phylogeography.In East Asia,plant distribution is greatly influenced by two phylogeographic breaks,the Mekong-Salween Divide and Tanaka-Kaiyong Line,however,few studies have investigated how these barriers affect the genetic diversity of species that are distributed across both.Here we used 14 microsatellite loci and four chloroplast DNA fragments to examine genetic diversity and distribution patterns of 49 populations of Populus rotundifolia,a species that spans both the Mekong-Salween Divide and the Tanaka-Kaiyong Line in southwestern China.Demographic and migration hypotheses were tested using coalescent-based approaches.Limited historical gene flow was observed between the western and eastern groups of P.rotundifolia,but substantial flow occurred across both the Mekong-Salween Divide and Tanaka-Kaiyong Line,manifesting in clear admixture and high genetic diversity in the central group.wind-borne pollen and seeds may have facilitated the dispersal of P.rotundifolia following prevalent northwest winds in the spring.We also found that the Hengduan Mountains,where multiple genetic barriers were detected,acted on the whole as a barrier between the western and eastern groups of P.rotundifolia.Ecological niche modeling suggested that P.rotundifolia has undergone range expansion since the last glacial maximum,and demographic reconstruction indicated an earlier population expansion around 600 Ka.The phylogeographic pattern of P.rotundifolia reflects the interplay of biological traits,wind patterns,barriers,niche differentiation,and Quaternary climate history.This study emphasizes the need for multiple lines of evidence in understanding the Quaternary evolution of plants in topographically complex areas.

关键词： Chloroplast DNA Microsatellite Phylogeographic break Populus rotundifolia Quaternary history wind direction

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Optimization Ensemble Weights Model for wind Forecasting System

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Computers, Materials & Continua 2022年第11期73卷 2619-2635页

作者： Amel Ali Alhussan El-Sayed M.El-kenawy Hussah Nasser AlEisa M.El-SAID Sayed A.Ward Doaa Sami Khafaga Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Electrical Engineering Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Delta Higher Institute of Engineering and Technology(DHIET) Mansoura35111Egypt Department of Electrical Engineering Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Faculty of Engineering Delta University for Science and TechnologyGamasaMansoura11152Egypt

Effective technology for wind direction forecasting can be realized using the recent advances in machine learning.Consequently,the stability and safety of power systems are expected to be significantly improved.However,the unstable and unpredictable qualities of the wind predict the wind direction a challenging problem.This paper proposes a practical forecasting approach based on the weighted ensemble of machine learning models.This weighted ensemble is optimized using a whale optimization algorithm guided by particle swarm optimization(PSO-Guided WOA).The proposed optimized weighted ensemble predicts the wind direction given a set of input features.The conducted experiments employed the wind power forecasting dataset,freely available on Kaggle and developed to predict the regular power generation at seven wind farms over forty-eight hours.The recorded results of the conducted experiments emphasize the effectiveness of the proposed ensemble in achieving accurate predictions of the wind direction.In addition,a comparison is established between the proposed optimized ensemble and other competing optimized ensembles to prove its superiority.Moreover,statistical analysis using one-way analysis of variance(ANOVA)and Wilcoxon’s rank-sum are provided based on the recorded results to confirm the excellent accuracy achieved by the proposed optimized weighted ensemble.

关键词： Guided Whale Optimization Algorithm(Guided WOA) forecasting machine learning weighted ensemble model wind direction

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Polarimetric scattering from a two-dimensional improved sea fractal surface

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Chinese Physics B 2010年第7期19卷 221-229页

作者：刘伟郭立新吴振森 School of Science Xidian University

This paper studies the influence of wind parameters and fractal dimension from an improved two-dimensional sea fractal surface on the polarimetric scattering by using facet integration. A two-dimensional improved sea surface simulated is discretized into three matrices of sea surface facets including a height matrix and two slope matrices on orthogonal directions. Based on the Kirchhoff approximation, the polarimetric scattered field is derived in the Cartesian coordinate system by integration of three matrices mentioned above. Finally, the fully polarised radar cross section is numerically simulated and the dependence of the polarimetric scattering on the sea fractal surface, such as the wind speed, the wind direction, as well as the fractal dimension, is discussed in detail.

关键词： polarimetric scattering sea fractal surface wind speed wind direction

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Adaptive Model Predictive Control for Yaw System of Variable-speed wind Turbines

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Journal of Modern Power Systems and Clean Energy 2021年第1期9卷 219-224页

作者： Dongran Song Qing Chang Songyue Zheng Sheng Yang Jian Yang Young Hoon Joo the School of Automation Central South Universityand Hunan Provincial Key Laboratory of Power Electronics Equipment and GridChangsha 410083China the School of Energy Science and Engineering Central South UniversityChangsha 410083China the School of IT Information and Control Engineering Kunsan National UniversityKunsan 54150Korea

Due to varying characteristics of the wind condition, the performance of the wind turbines can be optimized by adapting the parameters of the control system. In this letter, an adaptive technique is proposed for the novel model predictive control(MPC) for the yaw system of the wind turbines. The control horizon is adapted to the one with the best predictive performance among multiple control horizons. The adaptive MPC is demonstrated by simulations using real wind data, and its performance is compared with the baseline MPC at fixed control horizon. Results show that the adaptive MPC provides better comprehensive performance than the baseline ones at different preview time of wind directions. Therefore, the proposed adaptive technique is potentially useful for the wind turbines in the future.

关键词： wind turbine yaw system wind direction adaptive model predictive control

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Optimal placement of wind turbines within a wind farm considering multi-directional wind speed using two-stage genetic algorithm

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Frontiers in Energy 2021年第1期15卷 240-255页

作者： A.S.O.OGUNJUYIGBE T.R.AYODELE O.D.BAMGBOJE Power Energy Machines and Drives(PEMD)Research Group Department of Electrical and Electronic EngineeringUniversity of IbadanNigeria

Most wind turbines within wind farms are set up to face a pre-determined wind direction.However,wind directions are intermittent in nature,leading to less electricity production capacity.This paper proposes an algorithm to solve the wind farm layout optimization problem considering multi-angular(MA)wind direction with the aim of maximizing the total power generated on wind farms and minimizing the cost of installation.A twostage genetic algorithm(GA)equipped with complementary sampling and uniform crossover is used to evolve a MA layout that will yield optimal output regardless of the wind direction.In the first stage,the optimal wind turbine layouts for 8 different major wind directions were determined while the second stage allows each of the previously determined layouts to compete and inter-breed so as to evolve an optimal MA wind farm layout.The proposed MA wind farm layout is thereafter compared to other layouts whose turbines have focused site specific wind turbine orientation.The results reveal that the proposed wind farm layout improves wind power production capacity with minimum cost of installation compared to the layouts with site specific wind turbine layouts.This paper will find application at the planning stage of wind farm.

关键词： optimal placement wind turbines wind direction genetic algorithm wake effect

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CFD-based analysis of urban haze-fog dispersion—A preliminary study

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Building Simulation 2021年第2期14卷 365-375页

作者： Yu Zhang Yong Yu K.C.S.Kwok Feng Yan School of Medicine Tsinghua UniversityBeijingChina School of Aerospace Engineering Beijing Institute of TechnologyBeijingChina Faculty of Engineering School of Civil EngineeringThe University of SydneySydneyNew South WalesAustralia First Hospital of Tsinghua University BeijingChina

This paper proposes a computational fluid dynamics(CFD)model,along with dimensionless quantitative assessment standard—air pollution residual time(APRT)for the evaluation of local haze-fog(HF)dispersion in a built environment.A low APRT value ensures good ventilation.A building group model that comprises high-rise business building,mid-rise office buildings,low-mid-rise residential buildings(at the center of the building group),a mid-rise recreational center,and a local road(open terrain),was scaled down(1:100)to simulate the HF dispersion process.The orientation of the building group was numerically modified to generate a wind incidence normal to the high-rise building,mid-rise buildings,recreational center,and road.The results showed that the orientation of the building group largely determines the APRT.The most favorable orientation can reduce APRT by more than 50%.Our results strongly suggested that in order to reduce the consequential negative effect of air pollution,future urban designs should undergo a comprehensive ventilation assessment to ensure a low APRT value.

关键词： air pollution residual time urban environment high-rise building wind direction computational fluid dynamics

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Characteristics of daily extreme wind gusts on the Qinghai-Tibet Plateau, China

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Journal of Arid Land 2018年第5期10卷 673-685页

作者： YAO Zhengyi LI Xiaoying XIAO Jianhua Key Laboratory of Desert and Desertification Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Lanzhou 730000 China Gansu Center for Sand Hazard Reduction Engineering and Technology Lanzhou 730000 China

Severe wind is a major natural hazard and a main driver of deserdficadon on the Qinghai-Tibet Plateau. Generally, studies of Qinghai-Tibet Plateau＇s wind climatology focus on mean wind speeds and its gust speeds have been seldom investigated. Here, we used observed daily maximum gust speeds from a 95- station network over a 5-year period （2008-2012） to analyze the characteristics of extreme wind speeds and directions by fitting Weibull and Gumbel distributions. The results indicated the spatial distribution of extreme wind speeds and their direction on the Qinghai-Tibet Plateau is highly variable, with its western portion prone to greater mean speeds of extreme wind gusts than its eastern portion. Maximum extreme wind speeds of 30.9, 33.0, and 32.2 m/s were recorded at three stations along the Qinghai Tibet Railway. Severe winds occurred mostly from November to April, caused primarily by the westerly jet stream. Terrain greatly enhances the wind speeds. Our spatial analysis of wind speed data showed that the wind speeds increased exponentially with an increasing altitude. We also assessed the local wind hazard by calculating the return periods of maximum wind gusts from the observational data based on the statistical extreme value distributions of these wind speeds. Further attention should be given to those stations where the yearly maximum daily extreme wind speed increased at a rate greater than that of mean value of daily extreme wind speeds. Severe extreme wind events in these regions of the plateau are likely to become more frequent. Consequently, building structural designers working in these areas should use updated extreme wind data rather than relying on past data alone.

关键词： extreme wind gusts wind direction wind hazard wind speeds Qinghai-Tibet Plateau

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