Wind Speed Frequency Distribution Research Articles

Impact of meteorological data factors and material characterization method on the predictions of leading edge erosion of wind turbine blades

Leading edge erosion of wind turbine blades is a major contributor to wind farm energy yield losses and maintenance costs. Presented is a multidisciplinary framework for predicting rain erosion lifetimes of wind turbine blades. Key aim is assessing the sensitivity of lifetime predictions to: modeling aspects (material erosion model, blade aerodynamics), input data and/or their preprocessing (joint frequency distribution of wind speed and droplet size based on synchronous site-specific measurements versus frequency distribution generated with partly site-agnostic modeling standards, wind speed records of nacelle anemometer or extrapolated at hub height from met masts), and environmental conditions (UV weathering). The analyses consider a Northwest England onshore site where a utility-scale turbine is operational, focus on a reference 5 MW turbine assumed operational at the site, and use a typical leading edge coating material. It is found that the largest variations in erosion lifetime predictions are due to material erosion model (based on rain erosion test data or fundamental material properties) and wind and rain model (measurement-based joint wind speed and droplet size distribution or standard-based modeled distribution). The use of joint wind and rain distribution also enables identifying wind/rain states with highest erosion potential, knowledge paramount to deploying erosion-safe turbine control.

Estimation of Weibull Probability Distribution Parameters with Optimization Algorithms and Foça Wind Data Application

In this study, the scale and shape parameters of the Weibull probability distribution function (W.pdf) used in determining the profitability of wind energy projects are estimated using optimization algorithms and the moment method. These parameters are then used to estimate the wind energy potential (WEP) in Foça region of İzmir in Turkey. The values of Weibull parameters obtained using Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Social Group Optimization (SGO), and Bat Algorithm (BA) were compared with the estimation results of the Moment Method (MM) as reference. Root mean square error (RMSE) and chi-square (χ^2) tests were used to compare the parameter estimation methods. The wind speed measurement values of the observation station in Foça were used. As a result of Foça speed data analysis, the annual average wind speed was determined as 6.15 m/s, and the dominant wind direction was found as northeast. Wind speed frequency distributions were compared with the measurement results and calculated with the estimated parameters. When RMSE and χ^2 criteria are evaluated together; it can be concluded that each used method behaves similarly for the given parameter estimation problem, with minor variations. As a result, it has been found that the optimization parameters produce very good results in wind speed distribution and potential calculations.

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.

Copula-based projections of wind power: Ireland as a case study

Wind energy is a key element in the ongoing push to decarbonize the energy supply. The first step in the development of a wind farm at a specific site is to conduct feasibility studies including accurate long-term wind potential assessments and estimates of the annual energy production. However, as evidence of climate change becomes more apparent recently, concerns about the planning and utilization of wind resources in the face of these new conditions have increased. Accurate projections are needed to determine the frequency distribution of wind speeds in an area and, on this basis, estimate the energy production. The purpose of this study is to analyze the wind resource, to estimate its potential and to prepare zoning maps of wind energy production to determine the most suitable sites for wind farms in Ireland. For this purpose, wind data from ten Global Circulation Models and different climate-change scenarios were used during the historical and future period of 1981–2010 and 2021–2050, respectively. These data were evaluated in the study area and then a multi-criteria decision-making method was applied to choose the best representative climate models over the area. In order to post-process the outputs of the selected models, 17 statistical distributions and 26 Copula families were applied. Results showed that the average wind speed in the region during the historical period is expected to decrease in 2021–2050 by approximately 2–7% based on the climate scenarios. Additionally, wind power density maps were produced for the study area.

THE POTENTIAL OF GREENHOUSE GAS EMISSION REDUCTION USING WIND ENERGY TECHNOLOGY AT SELECTED SITES IN MALAYSIA

Wind energy is one of the five fuels that can support the reduction of greenhouse gases and climate change impact. Wind energy is one of the most promising renewable energy sources that can be exploited to succeed in 1/20 of the renewable energy mix in Malaysia. This study aimed to determine the value of GHG savings on wind energy potential based on Meteorological Aerodrome Report (METAR) data in Malaysia to establish a suitable wind farm in Kudat and Langkawi Island that can potentially generate electricity. The wind speed data were collected from the METAR report broadcasted hourly. Wind speed extrapolation was based on the selected wind turbine generator (WTG) with 950 kW and 60 kW capacity. The prospect of wind energy was analysed using the Weibull distribution to determine the parameter of shape (k) and scale (c) from the wind speed frequency distribution. Finally, the Annual Energy Production (AEP) and the GHG savings were estimated. Based on the study, the annual reduction of GHG emissions ranges from 150 to 1,300 tonnes of CO2 per year. The ten-year projection roughly estimates about 1,500 to 13,000 tonnes of CO2 per year. Implementing a low-carbon, cost-effective energy source would undoubtedly enhance Malaysia’s sustainable energy system, as proposed by the United Nations Sustainable Development Goals 7.

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

The Mixture of Probability Distribution Functions for Wind and Photovoltaic Power Systems Using a Metaheuristic Method

The rising use of renewable energy sources, particularly those that are weather-dependent like wind and solar energy, has increased the uncertainty of supply in these power systems. In order to obtain considerably more accurate results in the analysis of power systems, such as in the planning and operation, it is necessary to tackle the stochastic nature of these sources. Operators require adequate techniques and procedures to mitigate the negative consequences of the stochastic behavior of renewable energy generators. Thus, this paper presents a modification of the original probability distribution functions (PDFs) where the original PDFs are insufficient for wind speed and solar irradiance modeling because they have a significant error between the real data frequency distribution and the estimated distribution curve. This modification is using a mixture of probability distributions, which can improve the fitting of data and reduce this error. The main aim of this paper is to model wind speed and solar irradiance behaviors using a two-component and a three-component mixture of PDFs generated from the integration of the original Weibull, Lognormal, Gamma, and Inverse-Gaussian PDFs. Three statistical errors are used to test the efficiency of the proposed original and mixture PDFs, which are the root mean square error (RMSE), the coefficient of correlation (R2), and the Chi-square error (X2). The results show that the mixture of PDFs gives better fitting criteria for wind speed and solar irradiance frequency distributions than the original PDFs. The parameters of the original and the mixture of PDFs are calculated using the innovative metaheuristic Mayfly algorithm (MA). The three-component mixture of PDFs lowered the RMSE by about 73% and was 17% more than the best original and the two-component mixture distributions.

Development of a numerical model for sector-average plume gamma dose and its validation with dose rate measurements at Kalpakkam NPP site, India

A Gaussian Plume based simple numerical model, named DIFFUSE is developed to simulate the long-term sector-average plume gamma dose due to radioactive plume released during normal operation of nuclear facilities. DIFFUSE calculates site specific joint frequency distributions of wind speed, wind direction and atmospheric stability using micrometeorological observations. It performs the finite sector-average dose integration for any stack height and gamma energy using Simpson's 1/3rd method with sufficient computational efficiency within the site boundary up to 2 km. Plume dose contribution to the main plume sector from nearest and next nearest side plume sectors is also calculated. DIFFUSE is validated with a 3-month long, starting from February 2021 to April 2021, dose rate observation data during operational releases from 100 m stack of Madras Atomic Power Station, Kalpakkam, India. Meteorological data from onsite 50 m tower and continuous dose rate observation from two sets of Autonomous Gamma Dose Logger (AGDL) detectors, namely n-AGDLs and r-AGDLs, placed in two different configurations along the geometric arcs of wind sectors around the stack are used. Simulated doses are compared with look-up table based dose estimates by Hukkoo et al. (1988). Linear spatial averaging of cumulative AGDL doses on a sector arc is used as measured sector-average dose for model validation. Simulations performed for both n-AGDL and r-AGDL configurations show DIFFUSE estimated doses are ∼37% lower and Hukkoo estimated doses are at least ∼50% lower than the measured doses. Statistical analysis of DIFFUSE simulated doses shows a statistical correlation of R2∼98.3%, slope of the fit ∼1.36 for n-AGDL setup and R2∼75.3%, slope of the fit ∼1.57 for r-AGDL setup. Overall, DIFFUSE produces conservative doses compared to look-up table based doses as required by regulatory bodies.

Estimation methods for wind power potential with practical case study

The potential at any location of the world for wind power must be appraised before it can be used effectively. The current state of wind resource assessment studies is provided in order to find appropriate methodologies. The Weibull distribution is a two- or three-parameter distribution function by which the wind speed's frequency distribution can be fitted properly . This set of curves has been proven to fit wind speed readings extremely well. The Maximum likelihood method, the Modified maximum likelihood method, Error of approximation, Method of Moment and the Energy pattern factor method are all offered as methods for estimating the parameters of the Weibull wind speed distribution for wind energy analysis. A sample wind speed data set is used to demonstrate the use of each method, and statistical methods of analysis are used to compare the accuracy of each method. The research aids in identifying which method is most effective in finding Weibull distribution parameters and determining the wind energy resource. In grid-connected wind producing plants, wind power forecasting is crucial for demand-supply equilibrium. Many accurate and dependable weather forecasting models use a range of modern methodologies. The electricity prediction is primarily dependent on short-term to second-by-second forecasting, intermediate duration of 2-7 days and with long-term prediction and short-term duration of nearly 2 days, with the help of various models.

Mixture probability distribution functions using novel metaheuristic method in wind speed modeling

Wind speed modeling is essential for predicting wind energy potential and properly utilizing wind power. The efficiency of the chosen probability distribution function (PDF) to describe the measured wind speed frequency distribution determines the quality of the wind speed assessment. The original probability distribution functions are insufficient in wind speed modeling as they have a significant error between real wind speed frequency distribution and the estimated distribution curve, while the mixture probability distributions can enhance the fitting of wind speed frequency distribution and reduce this error. The objective of this work is to model wind speed characteristics using two and three components mixture probability distribution functions generated from the combination of original Weibull, Gamma, and Inverse Gaussian distribution functions. To examine the fitness of the probability distribution functions, statistical errors such as the root mean square error (RMSE), Chi-square error (X2), and coefficient of Correlation (R2) are used as judging criteria. Results indicate that the two-component mixture Weibull distribution gives the best fitting among the original and the other two-component mixture PDFs. Moreover, the results show that the three-component mixture Weibull distribution gives the best fitting among all distributions proposed in this work. The mixture distribution functions used in this study have a higher level of complexity than the original distribution in estimating parameters as they have a higher number of optimization variables that need an artificial intelligence optimization method. The novel metaheuristic Aquila Optimizer (AO) method is used to estimate the parameters of proposed original and mixture PDFs in this work.

IROGI–PNSE Based Control of Grid-Interactive DFIG Under Unbalanced Network With Wind Power Leveling Capability

Unbalanced grid voltages, appearing at stator terminals of a doubly fed induction generator based wind energy conversion system, deteriorate its performance by instigating unwanted oscillations and distortions in output grid power and rotor currents, respectively, as well as by introducing unbalanced currents in the stator and the grid. This article aims to address these challenges using a coordinated control strategy based on an improved reduced order generalized integrator (IROGI) for positive and negative sequence estimation (PNSE). The IROGI–PNSE further enhances the control performance by eliminating the effects of dc component or unbalance in sensed voltages, on the grid synchronization. The coordinated control strategy uses hysteresis scheme for current control. The hysteresis-based current control requires fewer rotational transformations, and eliminates the need for PI/PIR regulators and notch filters for inner current control loops. This renders the control, computationally efficient. Moreover, the presented system is equipped to exhibit wind power leveling functionality through a battery energy storage. An approach, employing the region-specific wind speed frequency distribution graph, is utilized to constitute the power leveling operation modes. The effectiveness of the control scheme is demonstrated through test results on a prototype developed in the laboratory.

Evaluation of the Technical Wind Energy Potential of Kisii Region Based on the Weibull and Rayleigh Distribution Models

Background. Global warming is a growing threat in the world today mainly due to the emission of CO2 caused by the burning of fossil fuel. Consequently, countries are being forced to seek potential alternative sources of energy such as wind, solar, and photovoltaic among many others. However, the realization of their benefits is faced with challenges. Though wind stands a chance to solve this problem, the lack of adequate site profiles, long-term behavioural information, and specific data information that enables informed choice on site selection, turbine selection, and expected power output has remained a challenge to its exploitation. In this research, Weibull and Rayleigh models are adopted. Wind speeds were analyzed and characterized in the short term and then simulated for a long-term measured hourly series data of daily wind speeds at a height of 10 m. The analysis included daily wind data which was grouped into discrete data and then calculated to represent the mean wind speed, diurnal variations, daily variations, and monthly variations. To verify the models, statistical tools of Chi square, RMSE, MBE, and correlational coefficient were applied. Also, the method of measure, correlate, and predict was adopted to check for the reliability of the data used. The wind speed frequency distribution at the height of 10 m was found to be 2.9 ms-1 with a standard deviation of 1.5. From the six months’ experiments, averages of wind speeds at hub heights of 10 m were calculated and found to be 1.7 m/s, 2.4 m/s, and 1.3 m/s, for Ikobe, Kisii University, and Nyamecheo stations, respectively. The wind power density of the region was found to be 29 W/m2. By a narrow margin, Rayleigh proves to be a better method over Weibull in predicting wind power density in the region. Wind speeds at the site are noted to be decreasing over the years. The region is shown as marginal on extrapolation to 30 m for wind energy generation hence adequate for nongrid connected electrical and mechanical applications. The strong correlation between the site wind profiles proves data reliability. The gradual decrease of wind power over the years calls for attention.

Wind speed frequency distribution modeling and wind energy resource assessment based on polynomial regression model

The single Weibull distribution, which is the most frequently used in evaluating wind energy feasibility, fits poorly with zero and low wind speed values and is ineffectual for multimodal wind speed data. Although some other existing models are effective for multimodal wind speed distribution, the gap remains large at zero and low wind speeds. To overcome the shortcomings of these distribution models, we propose a polynomial regression model as the probability density function for wind speed frequency. The model parameters are obtained through linear least-square method. Comparison experiments were conducted between the single Weibull distribution model and the proposed approach. Then the optimal polynomial regression model, the mixture of two truncated normal distributions, and the tri-Weibull mixed model are compared from fitting performance. Meanwhile, the annual energy production and the cost of wind power generation are calculated based on the wind speed frequency distribution model. The results show that the proposed polynomial regression model provides excellent approximation for different wind speed frequency distributions. The approach can be used to fit zero and low wind speed data, and fit multimodal distribution data. When estimating the annual energy production and the wind power cost, our proposed approach has smaller modelling errors and lower cost ofpowergeneration than those from another methods. The proposed wind frequency distribution probability density function model would be very useful for wind energy resource assessment and development.

Evaluation of Wind Energy Potential in a Sahelian Zone: A Case Study of Ouahigouya, Burkina Faso

This study investigates the characteristics of the wind energy potential in Ouahigouya, Burkina Faso. For this purpose, eleven-years data (2006-2016) of wind speed (at 10 m above ground level (AGL)) and surface temperature data from Burkina Faso meteorological agency and wind speed (50 m AGL) from the NASA were used. Based on the mean vertical profile of wind speed at monthly and annual scales, the Weibull function was used to characterize the wind speed frequency distribution and to calculate the wind power density. Major results indicate that the peaks of mean wind speeds are estimated at 3.51 m.s-1 (May) and 5.18 m.s-1 (March) for the 20 m and 50 m altitudes respectively. The mean annual wind speed at the 20 m AGL at the site is estimated at 2.87 m.s-1 while at 50 m AGL, a value of 4.33 m.s-1 is recorded. The estimated wind power density reveals that the largest amounts of energy are generally collected during the months corresponding to the dry season with peaks in March or May depending on the height. The amount of energy available at 20 m AGL varies from 32.90 W/m2 (September) to 84.97 W/m2 (May). At 50 m AGL, this value varies respectively from 68.68 W/m2 (August) to 346.47 W/m2 (March). Average annual power density amounts are estimated at 65.92 W/m2 at 20 m and 223.17 W/m2 at 50 m, respectively, for a growth rate of 70.46 %. In view of these results, the Ouahigouya site could be conducive to the installation of small and medium-sized turbines to supply rural communities with electrical energy.

The Comparison of the Efficiency of Small Wind Turbine Generators with Horizontal and Vertical Axis Under Low Wind Conditions

Abstract The authors perform a comparative analysis of the efficiency of two types of low-power wind energy conversion systems with horizontal and vertical axis in the meteorological conditions of Latvia. The analysis is based on long-term wind speed measurements over the period of two years conducted by a network of 22 observation stations at the height of 10 m above the ground. The study shows that in the conditions of Latvia wind turbines with a horizontal axis are expected to work with greater efficiency than similar installations with a vertical axis. The paper presents the models of the spatial distribution of average wind speed, Weibull wind speed frequency distribution parameters and the values of the expected operational efficiency for small wind turbine generators. The modelling results are presented in the form of colour contour maps. Overall, the results of the study can serve as a tool for forecasting annual energy production and for estimating the feasibility of commercial use of wind energy at the height of 10 m in the territory of Latvia.

Improving mesoscale wind speed forecasts using lidar-based observation nudging for airborne wind energy systems

Abstract. Airborne wind energy systems (AWESs) aim to operate at altitudes above conventional wind turbines where reliable high-resolution wind data are scarce. Wind light detection and ranging (lidar) measurements and mesoscale models both have their advantages and disadvantages when assessing the wind resource at such heights. This study investigates whether assimilating measurements into the mesoscale Weather Research and Forecasting (WRF) model using observation nudging generates a more accurate, complete data set. The impact of continuous observation nudging at multiple altitudes on simulated wind conditions is compared to an unnudged reference run and to the lidar measurements themselves. We compare the impact on wind speed and direction for individual days, average diurnal variability and long-term statistics. Finally, wind speed data are used to estimate the optimal traction power and operating altitudes of AWES. Observation nudging improves the WRF accuracy at the measurement location. Close to the surface the impact of nudging is limited as effects of the air–surface interaction dominate but becomes more prominent at mid-altitudes and decreases towards high altitudes. The wind speed frequency distribution shows a multi-modality caused by changing atmospheric stability conditions. Therefore, wind speed profiles are categorized into various stability conditions. Based on a simplified AWES model, the most probable optimal altitude is between 200 and 600 m. This wide range of heights emphasizes the benefit of such systems to dynamically adjust their operating altitude.

Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India

In this paper the multiverse optimization (MVO) was used for estimating Weibull parameters. These parameters were further used to analyze the wind data available at a particular location in the Tirumala region in India. An effort had been made to study the wind potential in this region (13°41′30.4″ N 79°21′34.4″ E) using the Weibull parameters. The wind data had been measured at this site for a period of six years from January 2012 to December 2017. The analysis was performed at two different hub heights of 10 m and 65 m. The frequency distribution of wind speed, wind direction and mean wind speeds were calculated for this region. To compare the performance of the MVO, gray wolf optimizer (GWO), moth flame optimization (MFO), particle swarm optimization (PSO) and other numerical methods were considered. From this study, the performance had been analyzed and the best results were obtained by using the MVO with an error less than one. Along with the Weibull frequency distribution for the selected region, wind direction and wind speed were also provided. From the analysis, wind speed from 2 m/s to 10 m/s was present in sector 260–280° and wind from 0–4 m/s were present in sector 170–180° of the Tirumala region in India.

Weibull Probability Distribution of Wind Speed for Gaza Strip for 10 Years

The need of clean and renewable energy, as well as the power shortage in Gaza strip with few wind energy studies conducted in Palestine, provide the importance of this paper. Probability density function is commonly used to represent wind speed frequency distributions for the evaluation of wind energy potential in a specific area. This study shows the analysis of the climatology of the wind profile over the State of Palestine; the selections of the suitable probability density function decrease the wind power estimation error percentage. A selection of probability density function is used to model average daily wind speed data recorded at for 10 years in Gaza strip. Weibull probability distribution function has been estimated for Gaza based on average wind speed for 10 years. This assessment is done by analyzing wind data using Weibull probability function to find out the characteristics of wind energy conversion. The wind speed data measured from January 1996 to December 2005 in Gaza is used as a sample of actual data to this study. The main aim is to use the Weibull representative wind data for Gaza strip to show how statistical model for Gaza Strip over ten years. Weibull parameters determine by author depend on the pervious study using seven numerical methods, Weibull shape factor parameter is 1.7848, scale factor parameter is 4.3642 ms-1, average wind speed for Gaza strip based on 10 years actual data is 2.95 ms-1 per a day so the behavior of wind velocity based on probability density function show that we can produce energy in Gaza strip.

Frequency Distribution Model of Wind Speed Based on the Exponential Polynomial for Wind Farms

This study introduces and analyses existing models of wind speed frequency distribution in wind farms, such as the Weibull distribution model, the Rayleigh distribution model, and the lognormal distribution model. Inspired by the shortcomings of these models, we propose a distribution model based on an exponential polynomial, which can describe the actual wind speed frequency distribution. The fitting error of other common distribution models is too large at zero or low wind speeds. The proposed model can solve this problem. The exponential polynomial distribution model can fit multimodal distribution wind speed data as well as unimodal distribution wind speed data. We used the linear-least-squares method to acquire the parameters for the distribution model. Finally, we carried out contrast simulation experiments to validate the effectiveness and advantages of the proposed distribution model.

Characterization of the near surface wind speed distribution at global scale: ERA-Interim reanalysis and ECMWF seasonal forecasting system 4

The present developments in 10 m wind seasonal forecast products have lead to a growth in the number of studies analysing different aspects of both its predictability and applicability. However, there is still a lack of global studies analysing the statistical properties of the probability distribution of 10 m wind speed comparing the seasonal forecast systems with the widely used reanalysis products. To fill this gap we have studied the properties of the probability distributions of 10 m wind speed from the ERA-Interim reanalysis and the European Centre for Medium-Range Weather Forecasts System 4 seasonal forecast system. We have focused on two seasons, JJA and DJF, considering both their interannual and intraseasonal variability. The 10 m wind speed distribution has been characterized in terms of the four main moments of the probability distribution (mean, standard deviation, skewness and kurtosis). We have also computed the coefficient of variation to identify the regions with the higher wind variability and the Shapiro–Wilks goodness of fit test to assess their normality. This set of parameters is important to provide useful climate information in wind energy decision-making processes that use simple assumptions of the wind speed frequency distribution to properly estimate the wind energy potential. Besides, this study also illustrates where the discrepancies of the distributions of the seasonal predictions and the reference dataset are higher and, thus, which might need special attention from a bias adjustment perspective.