Wind Speed Estimation Research Articles

Profit enhancement and imbalance cost reduction with solar PV-fuel cell-EV hybrid system in a wind-integrated day-ahead power market

Abstract In a wind-integrated deregulated network, the wind farm must intimate the power-generating capacity bids to the market controller at least one day before it begins operations. The wind farm's bid submissions are based on the estimated wind speed (EWS); nevertheless, slight differences between the real wind speed (RWS) and the EWS will result in penalties or incentives imposed by the Independent System Operator (ISO). This occurrence is known as the power market imbalance cost, and it has a direct influence on the system's profitability. To mitigate this effect, solar PV and fuel cell storage technologies are used with the wind farm to enhance system profit by offsetting the negative effects of the imbalance cost. Here, solar PV and fuel cells are used to function in the required time i.e. operate in the charging mode when the RWS is greater than the EWS and in discharging mode when the EWS is greater than the RWS to balance the power supply in the grid as to fulfill the power bidding conditions. Furthermore, the study focuses on minimizing potential system risks, which have been assessed using several risk assessment tools such as Value-at-Risk (VaR) and Cumulative Value-at-Risk (CVaR). The work was conducted using an IEEE 14 bus test system. Initially, the solar PV-fuel cell system supplies power to meet local needs, and the remaining energy is sent to the grid to maximize the system's profit. Electric vehicles (EVs) have also been incorporated to maximize the system economy in more quantities and to reduce the system risk further as compared to the solar PV-fuel cell operation. Three different optimization methods, i.e. AGTO (Artificial Gorilla Troops Optimizer Algorithm), ABC (Artificial Bee Colony Algorithm), and SQP (Sequential Quadratic Programming), were used in a comparative analysis to assess the effectiveness of the proposed approach.

Regionalized ensemble estimation of wave periods for assessing wave energy resources across Canada. Part I: Improved wave-period modelling methodology

Large-scale estimations of wave periods are desired for wave energy assessment, ocean engineering, and wave climate research. Long-term global wave data from satellite altimeters are routinely applied to the estimations. However, this is challenged by uncertainties in wave-period models (WPMs), inaccuracies in data, and simplifications in modeling. Additionally, there exists a gap in the comprehensive examination of the variational mechanisms governing wave periods or model performances. As an effort to address them, we innovate a macroscale regionalized ensemble wave-period modeling (MREWPM) method by optimizing four wave-period models, driven by enhanced altimeter-based REWS (regionalized ensemble wave simulation) estimates of wave heights and wind speeds, within a regionalization framework in macroscale water environments. Results show that MREWPM driven by REWS dataset outperforms existing methods and performs better at larger scales (e.g., in eliminating local-scale overestimation). WPMs are more accurate over remote, deep, and windy regions in cool seasons under metrics-, scale- and data-dependent variations of performances with driving factors (mainly geographical features). This study serves as a foundational contribution towards the enhancement of wave-period simulations, the advancement of understanding wave-period dynamics, and the scientific evaluation of wave energy at macroscales.

Regional frequency analysis of extreme wind in Pakistan using robust estimation methods

The quantile estimates of extreme wind speed are needed for various areas of interest using regional frequency analysis (RFA) and extreme value theory. These calculations are crucial for the coding of wind speed. The data was taken from the NASA official website at a 10-meter distance and measured in meters per second (m/s). RFA of annual maximum wind speed (AMWS) using L-moments is performed utilizing annual maximum wind speed data from sixteen sites (16) in Pakistan’s Khyber Pakhtunkhwa province. There are no sites that are found to be discordant. The wards method is used to construct a homogenous region and make two homogenous regions from 16 sites. The heterogeneity test justifies that both clusters are homogeneous. The most appropriate probability distribution from the Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type-3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV) distributions is chosen to calculate regional quantiles. According to the L-moments diagram and Z statistics, the GEV for Cluster- Ι and GLO for Cluster- ΙΙ are the best suggestions from the others. Both clusters’ robustness is measured utilizing relative bias (RB) and relative root mean square error (RRMSE). Overall, GEV distribution is fit for cluster-Ι, and the GLO distribution is fit for cluster-ΙΙ. Utilizing the site mean and median as index parameters, we can also find at-site quantiles from regional quantiles. The study’s quantile estimates can be employed in codified structural designs with policy consequences.

Characterizing surface pressure and wind fields of typhoons approaching Hong Kong

In this paper, 17 severe typhoons that have affected Hong Kong are simulated using an advanced numerical atmospheric simulation system - Weather Research and Forecasting model (WRF). The simulated surface pressure and wind fields of these typhoons are validated against a wide range of field observations. Then azimuth-dependent models for the radius of maximum winds and the Holland parameter are established statistically at the surface level. It is observed that the shape parameter of the Holland pressure model is smaller at the surface than that at the gradient level. And the Holland wind field model cannot well reproduce the simulated radial wind profiles due to the complexities of nonuniform surface conditions and typhoon dynamics. It is found that the modified Rankine model provides satisfactory estimates of typhoon wind speeds in Hong Kong. Additionally, wind field asymmetries of typhoons approaching Hong Kong are highly correlated with the typhoon track velocity, vertical wind shear and the angle between them. The proposed statistical models and identified characteristics of wind field asymmetries of typhoons will provide useful information for rapidly assessing typhoon wind hazards.

A Novel Method for the Estimation of Sea Surface Wind Speed from SAR Imagery

Wind is one of the important environmental factors influencing marine target detection as it is the source of sea clutter and also affects target motion and drift. The accurate estimation of wind speed is crucial for developing an efficient machine learning (ML) model for target detection. For example, high wind speeds make it more likely to mistakenly detect clutter as a marine target. This paper presents a novel approach for the estimation of sea surface wind speed (SSWS) and direction utilizing satellite imagery through innovative ML algorithms. Unlike existing methods, our proposed technique does not require wind direction information and normalized radar cross-section (NRCS) values and therefore can be used for a wide range of satellite images when the initial calibrated data are not available. In the proposed method, we extract features from co-polarized (HH) and cross-polarized (HV) satellite images and then fuse advanced regression techniques with SSWS estimation. The comparison between the proposed model and three well-known C-band models (CMODs)—CMOD-IFR2, CMOD5N, and CMOD7—further indicates the superior performance of the proposed model. The proposed model achieved the lowest Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), with values of 0.97 m/s and 0.62 m/s for calibrated images, and 1.37 and 0.97 for uncalibrated images, respectively, on the RCM dataset.

Zynq FPGA for hardware co-simulation of Takagi-Sugeno neuro-fuzzy for MPPT algorithm incorporating sensorless wind speed estimation in grid-connected wind system

This paper presents a hardware implementation on the Field Programmable Gate Array (FPGA) Zed-Board of a Maximum Power Point Tracking (MPPT) incorporating pitch angle control system and sensorless wind speed estimation using the Takagi-Sugeno (TS) Adaptive Neuro-Fuzzy Inference System (ANFIS). The described approach is applied specifically to a grid-connected Permanent Magnet Synchronous Generator-based Variable Speed Wind Turbine (VSWT). Firstly, the paper proposes an estimator-based TS-ANFIS model for real-time wind speed estimation, addressing challenges with conventional anemometers, such as precision, and susceptibility to adverse weather conditions. The estimated wind speed guides the calculation of an optimized mechanical speed for MPPT control. Secondly, an MPPT-based TS-ANFIS controller is introduced to achieve the maximum power point, integrating pitch angle control to prevent turbine failures in high wind speeds. Finally, the paper emphasizes the hardware implementation on the FPGA Zed-Board, leveraging its parallel processing capabilities to enhance control system quality by reducing sampling periods and loop delays. Validation includes simulations in Matlab/Simulink using the Xilinx system generator and hardware co-simulation on the FPGA Zed-Board. A comparative analysis highlights the contributions and advancements of the proposed models and controllers compared to recent schemes in the field. Indeed, the proposed MPPT-based TS-ANFIS approach effectively maximizes the power extracted from the VSWT, achieving an estimated average efficiency of 99.84 %. In contrast, PID methods show average efficiencies of 92.63 %. Additionally, compared to other published works, the proposed MPPT-based TS-ANFIS method demonstrates a rapid response time of 0.001 s and lower static error at 0.02 %. Furthermore, the proposed WSE-based TS-ANFIS model exhibits superior performance and effectiveness, yielding a root mean squared error (RMSE) of 0.0085381, a determination coefficient (R2) value of 0.99985, and a Pearson correlation coefficient (r) value of 0.99996. Moreover, the proposed hardware implementation of these approaches maintains lower power usage, operating at a high frequency of 80.38 MHz and achieving a high throughput of 2572.16 Mbps.

Advancements in marine atmospheric parameters measurement: integrated dual-channel LIDAR for temperature, humidity, and wind speed estimation

Advancements in marine atmospheric parameters measurement: integrated dual-channel LIDAR for temperature, humidity, and wind speed estimation

Crop yield prediction with environmental and chemical variables using optimized ensemble predictive model in machine learning

Abstract Enhanced crop yield prediction is necessary for agronomists to make dynamic premonsoon decisions. The input variables precipitation, temperature, evaporation, wind speed, and chemical use influence crop yield estimations. In this study, we analyzed the correlation between crop yield and input features, and scaled up the prediction power of the crop yield model using optimized ensemble learning for machine learning. The proposed model is expected to deal with the limitations of existing models by minimizing effort and data requirements. It achieved better performance than the other approaches with a MSE (Mean Squared Error) of 42963, MAE (Mean Absolute Error) of 87, and R 2 (Coefficient of Determination) of 0.96. The findings of this study have important suggestions for agricultural management and policy-making. The proposed model offers possible applications for enhancing crop yield prediction across various perspectives, thereby assisting more informed decision-making in agriculture.

Comparative examinations of wind speed and energy extrapolation methods using remotely sensed data – A case study from Hungary

Exact knowledge of wind energy potential is a fundamental issue in wind energy utilization. The vertical changes in wind speeds, that is, the wind profile, have a predominant impact on the wind energy available at a location because the kinetic energy of moving air is proportional to the square of the wind speed. Roughness describes the resistance of a 3D surface to moving air. The exponent α of the power law of Hellmann and the roughness length (z0) are two parameters that describe the effects of the roughness of the surface on the wind profile. They can be used for the vertical extrapolation of wind speeds. The exponent α can be determined using multiple height level wind speed measurement data, whereas a reliable technique for the calculation of the roughness length requires detailed knowledge of the 3D geometry of the measurement site. In the present study, the exponent α was calculated based on SODAR wind speed measurements, while (z0) was determined using a combination of GIS and UAS-based aerial survey methods. Wind speeds measured at 50 m were extrapolated for height levels of 80, 90, 100, 110, and 120 m using dynamic power law exponent values. Wind power was determined using the power law (method V1), roughness length (method V2), frequency distribution (method W-RF), and gamma distribution (method W-G), and Windographer software was compared to the values calculated from the empirical (measured) wind speeds. A comparative statistical analysis of the datasets of the power law and roughness length methods on monthly/diurnal, annual/diurnal, and month/direction contexts showed no significant differences at all height levels. Differences can be detected in the distribution of the signs of the differences at heights of 80 and 120 m for the entire dataset. Underestimation was dominant with a significant frequency (over 70 %) in the case of both methods and heights. There were no significant differences between the wind power estimations provided by the different methods, and all the methods involved in the study underestimated the wind speeds and wind energy potential for each height level. Methods V1 and V2 can be used alternatively, depending on the input data available for analysis. The major advantage of method V2 is that it provides the same accuracy as V1, which requires a UAS-based aerial survey at the beginning, but continuous wind measurements must be performed at a lower height only. This means that there is no need for a high measurement tower, which makes the measurements simpler, more cost-effective, and causes much less disturbance to the environment. Another important advantage of the methods presented here is that they use a dynamic approach of power law exponent values that provide a more realistic estimation of wind speed and energy on a diurnal scale.

Comparison of the Wind Speed Estimation Algorithms of Wind Turbines Using a Drive Train Model and Extended Kalman Filter

To compare and validate wind speed estimation algorithms applied to wind turbines, wind speed estimators were designed in this study, based on two methods presented in the literature, and their performance was validated using the NREL 5MW model. The first method for wind speed estimation involves a three-dimensional (3D) look-up table-based approach, constructed using drive train differential equations. The second method involves applying a continuous–discrete extended Kalman filter. To verify and compare the performance of the algorithms designed using these different methods, feed-forward control algorithms, available power estimation algorithms, and a linear quadratic regulator, based on fuzzy logic (LQRF) control algorithms, were selected and applied as verification means, using the estimated wind speed as the input. Based on the simulation results, the performance of the two methods was compared. The method using drive train differential equations demonstrated superior performance in terms of reductions in the standard deviations of rotor speed and electrical power, as well as in its prediction accuracy for the available power.

Short-term prediction of horizontal winds in the mesosphere and lower thermosphere over coastal Peru using a hybrid model

The mesosphere and lower thermosphere (MLT) are transitional regions between the lower and upper atmosphere. The MLT dynamics can be investigated using wind measurements conducted with meteor radars. Predicting MLT winds could help forecast ionospheric parameters, which has many implications for global communications and geo-location applications. Several literature sources have developed and compared predictive models for wind speed estimation. However, in recent years, hybrid models have been developed that significantly improve the accuracy of the estimates. These integrate time series decomposition and machine learning techniques to achieve more accurate short-term predictions. This research evaluates a hybrid model that is capable of making a short-term prediction of the horizontal winds between 80 and 95 km altitudes on the coast of Peru at two locations: Lima (12°S, 77°W) and Piura (5°S, 80°W). The model takes a window of 56 data points as input (corresponding to 7 days) and predicts 16 data points as output (corresponding to 2 days). First, the missing data problem was analyzed using the Expectation Maximization algorithm (EM). Then, variational mode decomposition (VMD) separates the components that dominate the winds. Each resulting component is processed separately in a Long short-term memory (LSTM) neural network whose hyperparameters were optimized using the Optuna tool. Then, the final prediction is the sum of the predicted components. The efficiency of the hybrid model is evaluated at different altitudes using the root mean square error (RMSE) and Spearman’s correlation (r). The hybrid model performed better compared to two other models: the persistence model and the dominant harmonics model. The RMSE ranged from 10.79 to 27.04 m s−1, and the correlation ranged from 0.55 to 0.94. In addition, it is observed that the prediction quality decreases as the prediction time increases. The RMSE at the first step reached 6.04 m s−1 with a correlation of 0.99, while at the sixteenth step, the RMSE increased up to 30.84 m s−1 with a correlation of 0.5.

Control of a 15MW off-shore wind turbine for black-start operation

Grid forming wind turbines are crucial for achieving a 100% renewable based grid. The mechanical control of the wind turbines is usually designed for maximum power tracking operation. This paper includes the development of a wind turbine pitch controller considering the wider operational range of a grid forming wind turbine. The proposed controller tracks the optimal generation characteristic of the wind turbine when grid connected and is designed to cater for the different power levels when the wind turbine is operated in islanded mode (or grid-connected at partial power). The proposed controller does not require wind speed estimation for its operation and shows better performance than standard pitch controllers during grid-forming operation, particularly, limiting wind turbine speed excursion during block de-loading. Moreover, the proposed controller does not increase the mechanical loads of the key wind turbine elements. The performance of the proposed control is validated in a black start procedure with a full wind farm realistic real-time simulator, including detailed electrical and aeroelastic models of the wind turbine generator, wind farm and connecting HVDC link. Time and frequency domain validation using the real-time model has shown the performance of the proposed controller during black start operation.

Hybrid CMOD-Diffusion Algorithm Applied to Sentinel-1 for More Robust and Precise Wind Retrieval

Synthetic Aperture Radar (SAR) imagery presents significant advantages for observing ocean surface winds owing to its high spatial resolution and low sensitivity to extreme weather conditions. Nevertheless, signal noise poses a challenge, hindering precise wind retrieval from SAR imagery. Moreover, traditional geophysical model functions (GMFs) often falter, particularly in accurately estimating high wind speeds, notably during extreme weather phenomena like tropical cyclones (TCs). To address these limitations, this study proposes a novel hybrid model, CMOD-Diffusion, which integrates the strengths of GMFs with data-driven deep learning methods, thereby achieving enhanced accuracy and robustness in wind retrieval. Based on the coarse estimation of wind speed by the traditional GMF CMOD5.N, we introduce the recently developed data-driven method Denoising Diffusion Probabilistic Model (DDPM). It transforms an image from one domain to another domain by gradually adding Gaussian noise, thus achieving denoising and image synthesis. By introducing the DDPM, the noise from the observed normalized radar cross-section (NRCS) and the residual of the GMF methods can be largely compensated. Specifically, for wind speeds within the low-to-medium range, a DDPM is employed before proceeding to another CMOD iteration to recalibrate the observed NRCS. Conversely, a posterior-placed DDPM is applied after CMOD to reconstruct high-wind-speed regions or TC-affected areas, with the prior information from regions characterized by low wind speeds and recalibrated NRCS values. The efficacy of the proposed model is evaluated by using Sentinel-1 SAR imagery in vertical–vertical (VV) polarization, collocated with data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Experimental results based on validation sets demonstrate significant improvements over CMOD5.N, particularly in low-to-medium wind speed regions, with the Structural Similarity Index (SSIM) increasing from 0.76 to 0.98 and the Root Mean Square Error (RMSE) decreasing from 1.98 to 0.63. Across the entire wind field, including regions with high wind speeds, the validation data obtained through the proposed method exhibit an RMSE of 2.39 m/s, with a correlation coefficient of 0.979.

Estimating Wind Speeds in Tornadoes Using Debris Trajectories of Large Compact Objects

Abstract Currently, the enhanced Fujita scale does not consider the wind-induced movement of various large compact objects such as vehicles, construction equipment, and farming equipment/haybales that are often found in postevent damage surveys. One reason for this is that modeling debris in tornadoes comes with considerable uncertainties since there are many parameters to determine, leading to difficulties in using trajectories to analyze wind speeds of tornadoes. This paper aims to develop a forensic tool using analytical tornado models to estimate lofting wind speeds based on trajectories of large compact objects. This is accomplished by implementing a Monte Carlo simulation to randomly select the parameters and plotting cumulative distribution functions showing the likelihood of lofting at each wind speed. After analyzing the debris lofting from several documented tornadoes in Canada, the results indicate that the method provides threshold lofting wind speeds that are similar to the estimated speeds given by other methods. However, the introduction of trajectories produces estimated lofting wind speeds that are higher than the EF-scale rating given from the ground survey assessment based on structural damage. Further studies will be required to better understand these differences.

Comparison of Tornado Damage Characteristics to Low-Altitude WSR-88D Radar Observations and Implications for Tornado Intensity Estimation

Abstract Given the obvious difficulties in directly sampling tornadic wind fields, ongoing work continues to improve estimates of near-ground wind speeds in tornadoes. This study builds upon a recently proposed empirical relationship between radar-observed velocities in the lowest 150 m above ground level (AGL) and the theoretical peak 15-m AGL wind speed. We create and analyze a dataset of 194 velocity observations within tornadoes in the lowest 150 m AGL. These observations are drawn from 105 individual tornadoes that occurred across a diverse range of EF-scale ratings (EF0–4), convective modes (discrete supercell and quasi-linear convective system), geographical regions, and housing unit densities (HUDs). Comparing the radar-estimated and damage-estimated tornado wind speeds, and corresponding EF- and F-scale ratings, is the primary focus of the ensuing analysis. Consistent with recent work, damage-estimated tornado wind speeds tend to be lower than radar-estimated near-surface wind speeds, especially for stronger tornadoes. Damage- and radar-estimated wind speed differences are not strongly related to the availability of damage indicators (as measured by HUD). While some relationship exists—particularly underestimates of peak wind speeds for strong–violent tornadoes in low HUD areas—the tendency of radar-based strong/violent tornado intensity estimates to be meaningfully higher than EF-scale-based damage estimates exists across the HUD spectrum. The legacy F-scale wind speed ranges may ultimately provide a better estimate of peak tornado wind speeds at 10–15 m AGL for strong–violent tornadoes and a better damage-based intensity rating for all tornadoes. These results are contextualized with regard to ongoing community efforts to improve tornado intensity estimation. Significance Statement Due to the numerous difficulties in collecting direct observations of tornado wind speeds, we compare methods that are used to estimate near-ground wind speeds using both radar measurements of wind speeds aloft and damage. Our results show that 1) the damage-estimated intensities of stronger tornadoes are more likely to be underestimates of true wind speeds than weaker tornadoes based on radar observations, 2) this bias is present for tornadoes that occur in more built-up areas as well as more sparsely populated ones, and 3) the legacy Fujita scale may provide better wind speed estimates in stronger tornadoes. These findings contribute to community-wide efforts to improve damage-based estimates of peak tornado wind speeds.

Aggregation of data from multiple recording stations for extreme wind analysis and prediction

Accurate, reliable and robust techniques for the probabilistic estimation of extreme wind speeds are essential for the design of structures for wind loading. Aggregating gust wind data from various stations with similar, homogeneous wind climates into a ‘superstation’ for hazard analysis has been employed since the 1980′s to reduce the effects of sampling errors. A concern that has been raised recently is that prediction biases may arise from such aggregation, when the data exhibit non-homogeneity due to inevitable short data lengths or imperfect homogeneity of the wind climates. By Monte Carlo simulation, we show that superstation aggregation is an unbiased technique for high recurrence level estimations when an appropriate fitting method is used, and the apparent biases are dependent on the method used for fitting the hazard model. To ensure homogeneity, we introduce a de–trending technique for minimizing any biases in the aggregated wind data. Four model-fitting methods for superstation analysis are compared, and shown that the introduced de-trending method is effective for eliminating the biases due to sampling errors and non-homogeneity.

Hybrid wind-solar energy potential modeling using ERA5 and solar irradiation data in google Earth Engine

Renewable energies play a significant role in mitigating the climate change impacts. Wind and solar energy are projected to supply larger amount to the total renewable energy capacity of many nations. Long-term wind and solar data availability and analysis is vital for sustainable renewable energy farming. In this research, we analyze wind speed u- and v-components at 10 m height of the ERA5 daily reanalysis aggregates at a mesh of 31 Km during a long period (2010–2020) over the Red Sea and the Gulf of Suez coasts of Egypt. Google Earth Engine (GEE) code is developed and used for the analysis. The prime objective is to derive estimates on the spatio-temporal variability of speed, direction, and the wind energy estimates in the area. Further, the solar energy potential has been evaluated using reference data of the PVOUT – Photovoltaic power potential (kWh/m2/day) data, at nearly 1 km resolution, which is normalized at the evaluated sites, and then used to rank sites with their solar energy potential. Evaluation of estimates is carried out at selected thirteen sites distributed along the coasts to better understand the hybrid wind-solar energy potential. Frequency analysis is carried out to the wind speed spatial estimates to demarcate highest frequency wind events potential for wind farming. Reference in-situ wind speed observations available at daily and monthly time scales for Hurghada weather station is used for investigating the reanalysis data performance. The Hurghada site had the highest average wind speed (6.7 m s−1), frequency of events (75 %), and mean daily (60.4 kWh) and annual (22.02 MWh) wind energy. The windiest time intervals were the September month, 2017 year, and the summer season. The north had wind energy directions of 130°, while the south had 110°. Trend analysis showed declining trends for northern locations and rising yearly wind speed estimates for Berenice (0.005) and Halayeb (0.002). El-Tor location had the highest solar energy output, with a daily mean of 5.52 kWh/m2 and an annual mean of almost 2015 kWh/m2. The Hurghada, Jemsa, G. Zeit, and El-Tor sites achieved the highest rankings in terms of hybrid wind-solar energy potential. The results can be very valuable for optimal location of hybrid solar-wind energy facilities and provide viable and promising solutions for sustainably meeting Egypt's growing clean energy demands.

Joint Modeling of Wind Speed and Power via a Nonparametric Approach

Power output from wind turbines is influenced by wind speed, but the traditional theoretical power curve approach introduces uncertainty into wind energy forecasting models. This is because it assumes a consistent power output for a given wind speed. To address this issue, a new nonparametric method has been proposed. It uses K-means clustering to estimate wind speed intervals, applies kernel density estimation (KDE) to establish the probability density function (PDF) for each interval and employs Monte Carlo simulation to predict power output based on the PDF. The method was tested using data from the MERRA-2 database, covering five wind farms in Brazil. The results showed that the new method outperformed the conventional estimation technique, improving estimates by an average of 47 to 49%. This study contributes by (i) proposing a new nonparametric method for modeling the relationship between wind speed and power; (ii) emphasizing the superiority of probabilistic modeling in capturing the natural variability in wind generation; (iii) demonstrating the benefits of temporally segregating data; (iv) highlighting how different wind farms within the same region can have distinct generation profiles due to environmental and technical factors; and (v) underscoring the significance and reliability of the data provided by the MERRA-2 database.

Application of polyhedral meshes in large-eddy simulation of building array flow fields within neutral and unstable boundary layers

Urbanization has intensified the urban heat island phenomenon, a critical concern for human habitation in recent decades. Analyzing flow fields around building arrays is vital for mitigating issues such as heatstroke and air pollution, thereby enhancing human comfort in urban environments. However, challenges in accuracy and computational efficiency persist in simulating urban thermal environments. This study evaluates how boundary layer meshes in polyhedral meshes affect wind and temperature distributions in neutral and unstable boundary layers, utilizing large-eddy simulation (LES). A total of ten cases were examined, including eight with local mesh refinement and two without. The study demonstrates that local mesh refinement is an effective strategy to reduce mesh count by approximately 60 %, thereby reducing computational time by 60 % using the same computational configuration. The performance of the polyhedral meshes was found to be comparable to that of the hexahedral meshes. For the mean velocity and temperature, the discrepancies between the hexahedral and polyhedral cases were within 10 %, while for the second-order statistics, the discrepancies were within 20 %. Within the neutral boundary layer, boundary layer meshes on the ground and building surfaces had minimal impact on the wind field. However, these meshes enhanced the accuracy of low percentile wind speed estimations by approximately 20 % within the unstable boundary layer. Moreover, this study lays the foundation for research on the thermal environment in actual urban areas and contributes to the guidelines for LES with non-isothermal inflow conditions.

Comparative Analysis of Estimated Small Wind Energy Using Different Probability Distributions in a Desert City in Northwestern México

In this paper, four probability functions are compared with the purpose of establishing a methodology to improve the accuracy of wind energy estimations in a desert city in Northwestern Mexico. Three time series of wind speed data corresponding to 2017, 2018, and 2019 were used for statistical modeling. These series were recorded with a sonic anemometer at a sampling frequency of 10 Hz. Analyses based on these data were performed at different stationarity periods (5, 30, 60, and 600 s). The estimation of the parameters characterizing the probability density functions (PDFs) was carried out using different methods; the statistical models were evaluated by the coefficient of determination and Nash–Sutcliffe efficiency coefficient, and their accuracy was estimated by the measured quadratic error, mean square error, mean absolute error, and mean absolute percentage error. Weibull, using the energy pattern factor method, and Gamma, using the Method of Moments, were the probability density functions that best described the statistical behavior of wind speed and were better at estimating the generated energy. We conclude that the proposed methodology will increase the confidence of both wind speed estimation and the energy supplied by small-scale wind installations.