Distribution Of Wind Direction Research Articles

Practical method for evaluating wind influence on autonomous ship operations (2nd report)

AbstractRecently, a considerable number of research and development projects have focused on automatic vessels. A highly realistic simulator is needed to validate control algorithms for autonomous vessels. For instance, when considering the automatic berthing/unberthing of a vessel, the effect of wind in such low-speed operations cannot be ignored because of the low rudder performance during slow harbor maneuvers. Therefore, a simulator used to validate an automatic berthing/unberthing control algorithm should be able to reproduce the time histories of wind speed and wind direction realistically. Therefore, in our first report on this topic, to obtain the wind speed distribution, we proposed a simple algorithm to generate the time series and distribution of wind speed only from the mean wind speed. However, for wind direction, the spectral distribution could not be determined based on our literature surveys, and hence, a simple method for estimating the coefficients of the stochastic differential equation (SDE) could not be proposed. In this study, we propose a new methodology for generating the time history of wind direction based on the results of Kuwajima et al.’s work. They proposed a regression equation of the standard deviation of wind direction variation for the mean wind speed. In this study, we assumed that the wind direction distribution can be represented by a linear filter as in our previous paper, and its coefficients are derived from Kuwajima’s proposed equation. Then, as in the previous report, the time series of wind speed and wind direction can be calculated easily by analytically solving the one-dimensional SDE. The joint probability density functions of wind speed and wind direction obtained by computing them independently agree well with the measurement results.

Revisiting the estimation of extreme wind speed considering directionality

In the estimation of extreme wind speed, the effect of wind direction is usually ignored. Nevertheless, as a vital parameter of the wind, it should be reasonably taken into consideration. Nowadays, the influence of wind direction can be considered through directional wind speed or directly regarding it as a variable. Based on wind speed data of climate stations, the wind directions are divided into uniform and non-uniform sectors. Copula function is then applied to establish the joint distribution of directional wind speed. The effect of the partition of directional sectors is discussed subsequently. Meanwhile, a joint distribution model of wind speed and wind direction based on copula function is also set up. According to the joint distribution, extreme wind speeds under specified wind directions are estimated based on conditional probability. Besides, extreme wind speeds considering wind directionality based on the aforementioned two methods are compared. Results show that the classification of wind direction will affect the estimation of extreme wind speed. In addition, the estimation of extreme wind speed under specified wind direction based on the joint distribution of wind speed and wind direction does not reflect the actual wind characteristics. Through the comparison of different methods considering directionality, the result based on the joint distribution of directional wind speed seems more reasonable.

A Circular-Linear Probabilistic Model Based on Nonparametric Copula with Applications to Directional Wind Energy Assessment.

The joint probability density function of wind speed and wind direction serves as the mathematical basis for directional wind energy assessment. In this study, a nonparametric joint probability estimation system for wind velocity and direction based on copulas is proposed and empirically investigated in Inner Mongolia, China. Optimal bandwidth algorithms and transformation techniques are used to determine the nonparametric copula method. Various parameter copula models and models without considering dependency relationships are introduced and compared with this approach. The results indicate a significant advantage of employing the nonparametric copula model for fitting joint probability distributions of both wind speed and wind direction, as well as conducting correlation analyses. By utilizing the proposed KDE-COP-CV model, it becomes possible to accurately and reliably analyze how wind power density fluctuates in relation to wind direction. This study reveals the researched region possesses abundant wind resources, with the highest wind power density being highly dependent on wind direction at maximum speeds. Wind resources in selected regions of Inner Mongolia are predominantly concentrated in the northwest and west directions. These findings can contribute to improving the accuracy of micro-siting for wind farms, as well as optimizing the design and capacity of wind turbine generators.

Study on the distribution of average wind speeds at a mountainous bridge site for structural durability design

Conventional wind speed distribution methods (e.g., Rayleigh distribution and Weibull distribution) may not adequately capture complex characteristics of wind fields in mountainous areas. To address this problem, this study proposes a semi-parametric mix method for modeling the distribution of average wind speeds based on the combination of nonparametric Kernel Density Estimation (KDE) and Generalized Pareto Distribution (GPD). In the proposed method, KDE focuses on capturing the distribution in the main part of average wind speeds, while GPD aims at performing the distribution in terms of those in the extreme part. The segment point (i.e., the threshold) between KDE and GPD distributions is determined based on the combination of conditional mean excesses criterion and empirical rule. Meanwhile, the selection of modeling parameters should ensure that the mix distribution model is continuous and differentiable at the identified threshold point. Then, the commonly-used conditional probability model is further introduced to describe the wind direction distribution. Finally, a case study based on the measured 10-min average wind speeds at a mountainous bridge site is employed to demonstrate the effectiveness of the proposed method. The results indicate that: (1) the distribution of omnidirectional average wind speeds in the mountainous bridge site exhibits an obviously single-peak characteristic, while those considering wind directionality present a certain bimodal characteristic; (2) the proposed method can effectively describe wind speed distributions with different statistical characteristics, and the fitting accuracy outperforms the frequently-employed Weibull distribution model.

Vine Copula-Based Multivariate Distribution of Rainfall Intensity, Wind Speed, and Wind Direction for Optimizing Qatari Meteorological Stations

This study employs copula functions to establish the dependency structure of the joint distribution among rainfall intensity, wind speed, and wind direction in Qatar. Based on a Vine Copula, the trivariate distribution between rainfall intensity, wind speed, and wind direction is found to exhibit a root-mean-square error (RMSE) of 0.0072 on the observed vs. modeled cumulative probabilities using ranked normalized observations. It is also found that the winter Shamal winds are most pronounced during rainfall. However, a secondary component of easterly winds known as the Kaus winds is also found to exert an important influence. This wind pattern is observable during rainfall at all the selected stations, albeit with minor variations. It is also found that rainfall stations where the rainfall is obstructed in any way from northwest to north and from east to southeast significantly influence the rainfall measurements. Specific rain gauges in Qatar are found to be situated in disrupted surroundings, such as meteorological stations close to passing traffic, where road spray could infiltrate the rain gauge funnel, impacting the accuracy of rainfall measurements. The study results necessitated the relocation of approximately half of these roadside gauges to mitigate wind-induced biases from road spray. An evaluation of operations is recommended for approximately 80 meteorological stations responsible for measuring rainfall in Qatar. The methodology devised in this study holds potential for application to other Middle Eastern countries and regions with similar climates.

Mesoscale modelling of North Sea wind resources with COSMO-CLM: model evaluation and impact assessment of future wind farm characteristics on cluster-scale wake losses

Abstract. As many coastal regions experience a rapid increase in offshore wind farm installations, inter-farm distances become smaller, with a tendency to install larger turbines at high capacity densities. It is, however, not clear how the wake losses in wind farm clusters depend on the characteristics and spacing of the individual wind farms. Here, we quantify this based on multiple COSMO-CLM simulations, each of which assumes a different, spatially invariant combination of the turbine type and capacity density in a projected, future wind farm layout in the North Sea. An evaluation of the modelled wind climate with mast and lidar data for the period 2008–2020 indicates that the frequency distributions of wind speed and wind direction at turbine hub height are skillfully modelled and the seasonal and inter-annual variations in wind speed are represented well. The wind farm simulations indicate that for a typical capacity density and for SW winds, inter-farm wakes can reduce the capacity factor at the inflow edge of wind farms from 59 % to between 54 % and 30 % depending on the proximity, size and number of the upwind farms. The efficiency losses due to intra- and inter-farm wakes become larger with increasing capacity density as the layout-integrated, annual capacity factor varies between 51.8 % and 38.2 % over the considered range of 3.5 to 10 MW km−2. Also, the simulated efficiency of the wind farm layout is greatly impacted by switching from 5 MW turbines to next-generation, 15 MW turbines, as the annual energy production increases by over 27 % at the same capacity density. In conclusion, our results show that the wake losses in future wind farm clusters are highly sensitive to the inter-farm distances and the capacity densities of the individual wind farms and that the evolution of turbine technology plays a crucial role in offsetting these wake losses.

Environmental Assessment of Toxic Gases and Particulate Matter (PM2.5) Monitoring During Fireworks Episodes (Diwali Festival) in Chennai Metropolitan, Southern India

The Indian celebration of lights, Diwali, is very important. This yearly event in October or November involves spectacular fireworks. Sparklers increase PM and harmful chemicals in the environment. It’s no surprise that events affect air quality. This study measured Total Gaseous Mercury (TGM), Particulate Matter (such as PM2.5), Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2), Nitric Oxide (NO), Carbon Monoxide (CO), Oxides of Nitrogen (NOx), and Ozone (O3) in Chennai, a densely populated metropolitan area in Tamil Nadu, India, before, on, and after Diwali. It was to determine how firecrackers affect air quality. Southwest (SW) and south-southwest winds predominate before Diwali. During Diwali, west-northwest and south-southwest wind speeds were highest. The average wind speed is 0.2-2.0 m/s. The average gaseous component concentration decreased pre- and post-Diwali. Wind direction and gaseous component distribution show Diwali sparklers were the main source of toxicity. A novel method measures wind patterns and velocities before, during and after Diwali to determine air quality dynamics. Gaseous component levels dropped significantly after Diwali, exceeding expectations. Firework emissions, meteorology and pollution dispersion were assessed. Public awareness efforts are needed since sparklers with lighting characteristics are the main source of Diwali pollution, according to the report. This new idea encourages sustainable, health-focused cultural events and urban environmental protection through data-driven decision-making.

Copula-based estimation of directional extreme wind speeds: Application for wind-resistant structural design

Accurate probabilistic modelling of wind loads has important implications to the wind-resistant design of structures. Whereas, most previous studies merely concerned the statistical analysis of wind speed, while the effect of wind direction was usually neglected in the wind load modelling. This paper thereby proposes a novel copula-based approach to construct the joint distribution of wind speed and direction for determining the directional extreme wind speed. Based on the wind records at six typical sites in Hong Kong, a series of parametric and nonparametric copula models are applied to model the joint probability density function (JPDF) of wind speed and direction. The goodness-of-fit test indicates that the Bernstein copula is the best model to describe the correlation between variables. Furthermore, by applying the superior copula model, directional extreme wind speeds with a 10-year return period at the six selected sites are determined on basis of the conditional probability theory. The results show that the estimated extreme wind speeds vary obviously among different directions. And, the estimated directional extreme wind speed may be unreliable in some directions when ignoring the joint effect of wind speed and direction in wind load modelling. Notably, the presented method is an effective and practical tool to estimate the directional extreme wind speed with a certain return period. This paper aims to provide useful reference for the wind-resistant design in tropical cyclone-prone regions.

Modelling Weather Precipitation Intensity on Surfaces in Motion with Application to Autonomous Vehicles.

With advances in the development of autonomous vehicles (AVs), more attention has been paid to the effects caused by adverse weather conditions. It is well known that the performance of self-driving vehicles is reduced when they are exposed to stressors that impair visibility or cause water or snow accumulation on sensor surfaces. This paper proposes a model to quantify weather precipitation, such as rain and snow, perceived by moving vehicles based on outdoor data. The modeling covers a wide range of parameters, such as varying the wind direction and realistic particle size distributions. The model allows the calculation of precipitation intensity on inclined surfaces of different orientations and on a circular driving path. The modeling results were partially validated against direct measurements carried out using a test vehicle. The model outputs showed a strong correlation with the experimental data for both rain and snow. Mitigation strategies for heavy precipitation on vehicles can be developed, and correlations between precipitation rate and accumulation level can be traced using the presented analytical model. A dimensional analysis of the problem highlighted the critical parameters that can help the design of future experiments. The obtained results highlight the importance of the angle of the sensing surface for the perceived precipitation level. The proposed model was used to analyze optimal orientations for minimization of the precipitation flux, which can help to determine the positioning of sensors on the surface of autonomous vehicles.

A probabilistic multi-objective design method of sail-photovoltaic-hybrid power system for an unmanned ocean surveillance trimaran

Considering the abundant wind and solar energy resources on waters, this paper integrates wind assistant propulsion and photovoltaic technology with ship electric power system as sail-photovoltaic-hybrid power system (sail-PV-HPS). When properly designed, sail-PV-HPS can present environmental and economic benefits compared with conventional sail-diesel power system. However, the optimal design of sail-PV-HPS is a complex task due to environmental uncertainties. In order to fill such gap, this paper proposes a probabilistic optimization method to determine the size parameters of sail-PV-HPS, pursing minimum GHG emission and lifecycle cost. Two pairs of joint distributions of wind speed and wind direction as well as solar irradiance and ambient temperature are established based on coupla function considering the interconnection and seasonal characteristics of the meteorology variables. The performance of the proposed approach is assessed by the retrofit of the power system of an unmanned ocean surveillance trimaran sailing in the Yellow Sea area. A deterministic optimization and a quasi-probabilistic optimization are performed to highlight the effect and importance of taking the uncertainties and their correlation into consideration. Results show that the sail-PV-HPS designed by the proposed probabilistic method outperforms the sail-PV-HPS given by the deterministic optimization and a quasi-probabilistic optimization, as well as, the original power system of the trimaran in terms of GHG emission and lifecycle cost.

Joint distribution of wind speed and direction over complex terrains based on nonparametric copula models

Understanding the distribution of wind field parameters over various terrains is important for kinds of applications including condition monitoring of high-speed railways and pollutant dispersion modeling. Based on Empirical Bernstein Copula (EBC), a nonparametric joint distribution model of wind speed and direction adaptable for complex wind field is proposed. Four commonly used parametric models are introduced for comparison, including the angular-linear model, Frank Copula, Gaussian Copula, and the model without considering interdependence. Yearly data measured from wind monitoring stations at 19 sites with various wind fields alongside the Lanzhou-Xinjiang high-speed railway in China is adopted for studying. The EBC model shows an overall best goodness-of-fit with the comprehensive metric value reaching 4.9586 (full score 5). Particularly, the superior performance of the EBC model is retained while the parametric models fail to predict the joint distribution in regions where wind speed and direction are highly dependent. In addition, when dealing with cases that the fitting goodness of marginal distribution is relatively poor (i.e. R2 = 0.488), a desired accuracy of joint distribution can be obtained with EBC. Further discussion about the optimal parameters and Copula structure of EBC is conducted to reveal reasons for the model showing adaptability to highly variable wind environments.

Layout optimization of an offshore floating wind farm deployed with novel multi-turbine platforms with the self-adaptive property

To deploy offshore wind in deeper waters, floating offshore wind confronts simultaneously significant opportunities and challenges. For dealing with the power deficit caused by the wake effect, a layout optimization method has been proposed for wind farms under variable wind conditions using the full-field wake model with the capacity to deploy multi-turbine platforms. The joint distribution of wind speed and direction is utilized to consider the variable wind conditions. The layout of an offshore wind farm, comprised of the floating platforms with multiple wind turbines and the wind-orientated ability, i.e., the self-adaptive property, is optimized under different wind conditions. Different numbers of turbines on a platform and platforms in the wind farm are deployed, respectively. The optimization results confirm that the platform with the self-adaptive property has a remarkable advantage to improve the efficiency of the wind farm. More turbines on a platform and less platforms can obtain higher efficiency. The distribution of wind speed and wind direction impacts the optimized layout and power output of the wind farm, simultaneously. The platforms arrange parallel lines which are perpendicular to the wind direction with more frequency and higher speed in the optimized wind farm. The concentration of the wind direction and the orthogonal angle between the wind and the long edge of the wind farm can increase the power output of the wind farm. However, the distribution of the wind speed has the dominant influence on the power output.

Unambiguous Wind Direction Estimation Method for Shipborne HFSWR Based on Wind Direction Interval Limitation

Due to its maneuverability and agility, the shipborne high-frequency surface wave radar (HFSWR) provides a new way of monitoring large-area marine dynamics and environment information. However, wind direction ambiguity is problematic when using monostatic shipborne HFSWR for wind direction inversion. In this article, an unambiguous wind direction measurement method based on wind direction interval limitation is proposed. The two first-order spectral wind direction estimation methods are first presented using the relationship between the normalized amplitude differences or ratios of the broadened Doppler spectrum and the wind direction. Moreover, based on the characteristic of a small wind direction estimation error in a large included angle between the spectral wind direction and the radar beam, the wind direction interval is obtained by counting the distribution of radar-measured wind direction within this included angle. Furthermore, the eliminated ambiguity of wind direction is transformed to judge the relationship between the wind direction interval and the two curves, which represent the relationship between the spreading parameter and the wind direction. Therefore, the remote sensing monitoring of ocean surface wind direction fields can be realized by shipborne HFSWR. The simulation results are used to evaluate the performance of the proposed method and the multi-beam sampling method for wind direction inversion. The experimental results show that the errors of wind direction estimated by the multi-beam sampling method and the equivalent dual-station model are large, and the proposed method can improve the accuracy of wind direction measurement. Three widely used wave directional spreading models have been applied for performance comparison. The wind direction field measured by the proposed method under a modified cosine model agrees well with that observed by the China-France Oceanography Satellite (CFOSAT).

A comprehensive performance evaluation methodology for sea-crossing cable-stayed bridges under wind and wave loads

This study proposes a probabilistic performance evaluation framework for sea-crossing cable-stayed bridges (SCBs) subjected to wind and wave excitations with the incorporation of wind speed and direction, bridge orientation, wind-wave correlation and parameter uncertainties. Both the bridge serviceability and safety are taken into account in the performance assessment. The fragility analysis of the SCB under wind and wave loads is conducted using the wind-wave correlation model constructed based on field data. The actual damage probability of a local SCB is computed by combining the fragilities of bridge under different wind incidence angles with the copula-based joint probability distribution (JPD) of wind speed and direction, which is developed using the wind field data recorded in Lianyungang city of China from 1971 to 2020. The results show that under the defined wind incidence angle of 0°, the exemplar SCB suffers the highest fragility. Moreover, with the data-based occurrence probability model of wind and wave hazards, the damage probabilities under all possible wind speeds and directions are computed; then the optimal and most unfavorable orientations of the SCB are obtained. This investigation provides a comprehensive and practical method for the performance evaluation and design optimization of SCBs under wind and wave loads with long-term recorded hazard data at the local site.

Local flow and loads estimation on wake-affected wind turbines using graph neural networks and PyWake

The estimation of the flow conditions throughout a modern wind farm is a relevant albeit complex problem involving several different modelling scales. In this context, recent years have seen the rise of the demand - normally industry-driven - of quick, computationally inexpensive, wind farm flow field simulators upon which further calculations might be undertaken (e.g. power output, fatigue loads, etc.). Naturally, the trustworthiness of such models hinges upon their ability to accurately estimate wakes, under the evident trade-off between computational time and accuracy. One such simulator that has been recently gaining traction within the wind community is PyWake, a Python-based flow solver, with several possible add-ons and modularity options. Based on runs from PyWake, this contribution studies the applicability of graph neural networks (GNN) as a simultaneous wind turbine fleet, wind inflow and loads surrogate. As graphs are discrete mathematical sets of dependent objects, one can see how the layout-dependent interaction between wind turbines’ aerodynamics, from which wake arises, lends itself to be modelled as a graph. Thus, we introduce the use of GNNs for layout-agnostic joint modelling of rotor-averaged wind speed, turbulence intensity, power production and damage equivalent loads (DEL) on individual turbines of wind farms. To this end, probabilistic samples of inflow conditions from a Weibull distribution for wind speed, uniform wind direction and conditional normal distributions of wind shear and nacelle yaw angles are generated as main inflow properties in the numerical simulations. Additionally, arbitrary wind farm layouts are created based on varied geometric shapes with random parametrization (varying orientations, length/width ratio). Both the arbitrary layouts and the random inflow conditions are then used as inputs for PyWake. PyWake’s output, namely, rotor averaged wind speed, turbulence intensity, power production and DELs of each individual wind turbine in the wind farms, are used to train a general GNN model. We elect to implement an Encode-Process-Decode GNN as a graph learning model. We compare the performance of various approaches of the relational dependency representation (edge-forming techniques) amongst the nodes (wind turbines) on the graph, such as Delaunay triangulation, KNN, radius-based methods and fully connected scheme. In our analysis, we evaluate the accuracy of GNNs and its ability to generalize their joint predictions for unseen layouts and varying inflow conditions.

The monthly average distributions of sea-surface wind speeds and directions in the East Vietnam Sea

This paper analyzes a monthly global gridded sea-surface wind dataset from Jan 1991 to Dec 2020, sourced from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS). The goal is to construct schematic maps depicting the monthly average distributions of sea-surface wind speeds and directions in the East Vietnam Sea. These maps provide a visual representation of the monsoon patterns at different points and areas in the region. The statistical and analytical findings reveal that the East Vietnam Sea is primarily influenced by two types of monsoons: Northeast and Southwest. When one is strong, the other is weak, and vice versa. The Northeast monsoon dominates from September to April of the following year, while the Southwest monsoon is more dominant from May to August. The intensity of the Northeast monsoon is usually greater than that of the Southwest monsoon. These results provide an overview of the monsoon distributions in the East Vietnam Sea, which can assist scientists and managers in proposing policies and measures suitable to the climate environment and wind seasons. These findings also help in the effective exploitation of abundant wind energy resources in coastal Vietnam and the East Vietnam Sea. Furthermore, the results are consistent with previous studies of monsoons in the East Vietnam Sea, indicating that the data source used in this study is reliable and can be utilized as input data for tidal, currents, or oil spread models.

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

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.

Assessment of Offshore Wind Resources, Based on Improved Particle Swarm Optimization

It is crucial to understand the characteristics of wind resources and optimize wind resources in the area that is being considered for offshore wind farm development. Based on the improved particle swarm optimization (IPSO) and the back propagation neural network (BPNN), the IPSO-BP hybrid intelligent algorithm model was established. The assessment of wind resource characteristics in the eastern waters of China, including average wind speed, extreme wind speed, wind power density, effective wind energy hours and wind direction distribution were all calculated. Additionally, the wind speed throughout the different years in Luchao Port, a famous seaport in China, was predicted. The results revealed that the wind power density is approximately 300 W/m2 all year round and that the effective wind energy hours take up about 92% per hour. It was also identified that the wind direction distribution is stable in Luchao Port, implying that there are better wind energy resource reserves in this region. The IPSO-BP model has a strong tracking performance for wind speed changes, and can accurately predict the wind speed change in a short period. In addition, the prediction error of the IPSO-BP model is smaller when the time of training data is closer to the target one, and it can be controlled within a 5% range.

A conditional approach for joint estimation of wind speed and direction under future climates

Abstract. This study develops a statistical conditional approach to evaluate climate model performance in wind speed and direction and to project their future changes under the Representative Concentration Pathway (RCP) 8.5 scenario over inland and offshore locations across the continental United States (CONUS). The proposed conditional approach extends the scope of existing studies by a combined characterization of the wind direction distribution and conditional distribution of wind on the direction, hence enabling an assessment of the joint wind speed and direction distribution and their changes. A von Mises mixture distribution is used to model wind directions across models and climate conditions. Wind speed distributions conditioned on wind direction are estimated using two statistical methods, i.e., a Weibull distributional regression model and a quantile regression model, both of which enforce the circular constraint to their resultant estimated distributions. Projected uncertainties associated with different climate models and model internal variability are investigated and compared with the climate change signal to quantify the robustness of the future projections. In particular, this work extends the concept of internal variability in the climate mean to the standard deviation and high quantiles to assess the relative magnitudes to their projected changes. The evaluation results show that the studied climate model captures both historical wind speed and wind direction and their dependencies reasonably well over both inland and offshore locations. Under the RCP8.5 scenario, most of the studied locations show no significant changes in the mean wind speeds in both winter and summer, while the changes in the standard deviation and 95th quantile show some robust changes over certain locations in winter. Specifically, high wind speeds (95th quantile) conditioned on direction in winter are projected to decrease in the northwestern, Colorado, and northern Great Plains locations in our study. In summer, high wind speeds conditioned on direction over the southern Great Plains increase slightly, while high wind speeds conditioned on direction over offshore locations do not change much. The proposed conditional approach enables a combined characterization of the wind speed distributions conditioned on direction and wind direction distributions, which offers a flexible alternative that can provide additional insights for the joint assessment of speed and direction.

Accurate and efficient forecasted wind energy using selected temporal metrological variables and wind direction

The aim of this work is to find the most efficient and suitable input features to be selected for forecasting monthly wind energy accurately. Machine learning is employed for a modular pipelined neural network, composed of time-delayed and feedforward networks with features of metrological variables such as atmospheric temperature, humidity, wind direction, and wind speed frequency distribution parameters. Logged data over a year’s period at a UAE site are analyzed on daily and monthly bases depending on their variation characteristics, in which standard Weibull probability distribution function is used for the feedforward neural network together with wind direction data, while daily average ambient temperature and humidity are attempted for the composite time delay networks. Different network abstractions of input features are compared, and it is found that wind direction data offer a better wind speed forecast. Wind energy is calculated based on monthly forecasting. A detailed adaptive probabilistic analysis is conducted to predict thresholds in variations of the forecast analysis. Error estimation tools are performed for adopting this method