Terms Of Wind Speed Research Articles

Short term wind speed forecasting using artificial and wavelet neural networks with and without wavelet filtered data based on feature selections technique

Wind speed forecasting plays a crucial role in enhancing the efficiency, reliability, and profitability of renewable energy systems. Accurate wind speed forecasting optimizes energy production and grid integration in renewable energy systems. It assists in maintenance scheduling, decision-making in energy markets, and supports risk management for financial planning. This paper investigates the difficult balance between model complexity and forecasting accuracy in wind speed forecasting using Artificial Neural Networks (ANNs) and Wavelet Neural Networks (WNNs). Employing a time series approach, the study develops models for 1-h-ahead wind speed forecasting, utilizing recent averaged data and exploiting correlations between consecutive wind speeds for improved short-term predictions. Models training involve the Backpropagation algorithm, with careful input variable selection to minimize errors. This study emphasizes that simpler ANN and WNN models can outperform complex ones and explores the effectiveness of wavelet filtering techniques and demonstrates the benefits of using wavelet filtering to enhance forecasting accuracy. The robustness of the developed models is validated through K-fold cross-validation, confirming the efficacy of the proposed models. Evaluation across two datasets demonstrates the superiority of the proposed models over the persistence model, with enhancements of 7–14% in RMSE. Additionally, the efficacy of wavelet transforms in enhancing forecasting accuracy compared to regular neural network predictions is emphasized, underscoring the effectiveness of wavelet filtering offering a strategic approach for optimizing wind power management in electrical grids.

Wavelet decomposition and neural networks: a potent combination for short term wind speed and power forecasting

The forecast of wind speed and the power produced from wind farms has been a challenge for a long time and continues to be so. This work introduces a method that we label as Wavelet Decomposition-Neural Networks (WDNN) that combines wavelet decomposition principles and deep learning. By merging the strengths of signal processing and machine learning, this approach aims to address the aforementioned challenge. Treating wind speed and power as signals, the wavelet decomposition part of the model transforms these inputs, as appropriate, into a set of features that the neural network part of the model can ingest to output accurate forecasts. WDNN is unconstrained by the shape, layout, or number of turbines in a wind farm. We test our WDNN methods using three large datasets, with multiple years of data and hundreds of turbines, and compare it against other state-of-the-art methods. It’s very short-term forecast, like 1-h ahead, can outperform some deep learning models by as much as 30%. This shows that wavelet decomposition and neural network are a potent combination for advancing the quality of short-term wind forecasting.

Bivariate reliability analysis for floating wind turbines

Abstract Wind turbines are designed to withstand extreme wind- and wave-induced loads, hence a reliability study is vital. This study presents a bivariate reliability approach, suitable for accurate assessment of critical forces and moments, occurring within the wind turbine’s critical mechanical parts, such as the drivetrain. A ecently developed bivariate modified Weibull method has been utilized in this study. Multivariate statistical analysis is more appropriate than a univariate one, as it accounts for cross-correlations between different system components. This study employed a bivariate modified Weibull method to estimate extreme operational loads acting on a 10-mega watt (MW) semi-submersible type floating wind turbine (FWT). Longitudinal, bending, twisting, and cyclic loads being among typical load types that FWTs and associated parts are susceptible to. Furthermore, environmental loads acting on an operating FWT being impacted by incoming wind’s stochastic behavior in terms of wind speed, direction, shear, vorticity, necessitates accurate nonlinear extreme load analysis for FWT critical parts such as the drivetrain. Appropriate numerical methods were used in this study to model dynamic, structural, aerodynamic, and control aspects of the FWT system. Bending moments acting on the FWT drivetrain have been obtained from SIMPACK (Multibody Simulation Method), given realistic in-situ environmental conditions. For a 5-year return period of interest, a bivariate modified Weibull method offered robust assessment of FWT’s coupled drivetrain’s bending moments.

Evaluation of the performance of five distribution functions for estimating Weibull parameters for wind energy potential in Nigeria

Wind energy is one of the most sought after worldwide among renewable energy sources. In order to determine the wind potential of a particular site, the wind speed has to be characterized and the Weibull parameters accurately determined. Characterization of wind speed in terms of Weibull parameters for wind energy potential in Nigeria is scarce. Several methods of determining Weibull parameters exist in literature. In this study, five numerical methods, Weibull-two (W), Mixture Weibull (MW), Gamma (G), lognormal (LN) and normal (N), were used to analyse wind speed data from twelve stations measured at 10 m height in order to determine the best method for estimating Weibull parameters for each station and region. The methods were compared and their accuracies were determined by three goodness-of-fit (GOF) tests namely Maximum error of Kolmogorov–Smirnov (K-S), root mean square error (RMSE) and Chi square (χ2). The performances of the methods were ranked on a scale of 1–5 and the results revealed that the MW was ranked the best in all the stations by two of the GOFs tests. It was closely followed by LN. The Weibull and Gamma methods performed poorly. Thus the MW is selected as the best method for determining Weibull parameters accurately and hence wind energy potential for Nigeria.

Modeling of urban canyons in tropical savannah climate: Relationship between geometric parameters and microclimate at pedestrian level

The population increase in cities has driven a gradual process of modification from the natural to the built environment. These transformations condition a typically urban climate, often characterized by an increase in air temperature, a phenomenon called urban heat island (ICU). In the search for sustainable and resilient cities, the literature points to the compact city model, and verticalization emerges as an alternative to make population demand viable. The objective of this investigation was to analyze the effects of the geometry of urban canyons on the microclimate, based on the H/W ratio and the orientation of the roads, taking as object of study the city of Arapiraca, Alagoas, with a tropical savannah climate. To this end, computational simulation was used in the ENVI-met v.4 Beta software in predictive analysis, based on 18 hypothetical scenarios. The scenarios vary in the application of initial and progressive minimum setback to the number of floors, with the incidence of predominant ventilation perpendicular (N = 0°) and oblique (N = 45°) to the buildings, for the hot and dry period. The performance of the canyon was evaluated by comparing air temperature, average radiant temperature and wind speed at 3 p.m., the period with the highest air temperature. The results revealed that in deep canyons with the use of the initial retreat, the extension of shaded area was greater, which decreased the mean radiant temperature. However, the scenarios with the use of progressive retreat showed better performance in terms of wind speed. In this case, the oblique orientation of the vias potentiated this result.

Short-Term Wind Speed and Power Forecasting for Smart City Power Grid With a Hybrid Machine Learning Framework

To address foreseeable challenges during the penetration of wind energy into the power grid, including accurate wind power forecasting and smart power generation scheduling, this study proposes a novel short-term wind speed forecasting model, named EMD-KM-SXL, which is based on empirical mode decomposition (EMD), K-means clustering and machine learning, and a new two-stage short-term wind power forecasting model based on wind speed forecasting and wind power curve modeling. The former wind speed forecasting model regards historical wind speed observations as model inputs, and the latter power forecasting model utilizes knowledge augmentation, introducing wind power conversion relationship, environmental factors as well as wind power system status parameters. In the proposed wind speed forecasting model, three machine learning models, including support vector regressor, XGBoost regressor, and Lasso regressor, are employed to forecast three types of frequency components that are generated via EMD and K-means clustering. Then, the wind power curve model is utilized to compute potential outpower based on the predicted wind speed, which is regarded as the first stage of the proposed wind power forecasting model. In the second stage, environmental factors and wind power system status parameters are introduced and an artificial neural network model, considering preliminary predicted power, environmental factors, and wind power system status parameters as model inputs, is built to make final power prediction. Computational results show that proposed models achieve the best performance in terms of wind speed and power forecasting over different forecasting horizons ranging from 10 to 40 minutes, compared with benchmarking methods.

Learning based short term wind speed forecasting models for smart grid applications: An extensive review and case study

This paper provides an extensive review of learning-based short-term forecasting models for smart grid applications. In addition to this, the paper also explores forecasting models including physical, statistical, hybrid, and uncertainty analysis models for wind speed forecasting. The learning-based models are classified into three broad categories, namely classical machine learning, advanced machine learning, and probabilistic learning. In this work, 41 different models are employed to forecast the wind speed. Dataset for this case study is collected from the site of Jodhpur, India. Dataset have 8759 sample with five features i.e., wind speed, pressure, humidity, temperature, and dew point. This forecast also includes the seasonal effects. Model accuracy has been tested considering single and multiple features in the input data. A comparative analysis of the performance of these 41 learning-based models is conducted based on coefficient of regression and error indices. It is observed that the performance of these models varies with the variability in the season. On the basis of the evaluation of these models, future recommendations are also framed out. These recommendations target of energy storage planning, energy market and policymakers, and reliability and reserve sizing direction. These recommendations can be utilized by authorities for effective planning and coordination of power.

Mutation based improved dragonfly optimization algorithm for a neuro-fuzzy system in short term wind speed forecasting

The Dragonfly algorithm (DA) is a heuristic optimization algorithm that is commonly used for complex optimization problems. Despite its widespread application, the abundance of social behaviors in its construct can lead to poor accuracy in solutions and an imbalance between exploration and exploitation phases. To overcome these issues, this paper proposes a mutation-based Dragonfly optimization algorithm (MIDA). In order to increase the solution accuracy of the original DA and reduce its handicaps, the proposed model includes three procedures, namely, mutation operation, boundary control, and greedy selection mechanisms. The mutation operator helps to find global optima by avoiding getting stuck at the local optimum point, while the boundary control and greedy selection mechanisms update the dragonflies at each iteration and thus use the better fitness value of the updated ones. The performance of the proposed MIDA is tested and shown to be superior to the original DA using several analyses such as convergence, search history, trajectory, average distance, computational complexity, diversity and balance. To validate the MIDA algorithm, it is tested on the 10, 30, 50 and 100 dimensions of the CEC2014 benchmark functions. Furthermore, a comparison against twelve state-of-the-art (SOTA) meta-heuristic optimization algorithms is performed. The performance of the proposed MIDA is also compared with the performances of some improved dragonfly algorithms taken from the literature. The statistical results obtained by the original DA and MIDA for ten minimization problems with 5, 10, 15 and 20 dimensions from the CEC2020 test suite are presented. Finally, the proposed algorithm is applied to optimize the ANFIS model parameters in order to be used for short-term wind forecasting as a real-world problem. The results obtained for the different dimensions of CEC2014 and CEC2020 test problems in the study show that the search performance of proposed MIDA is better than that of the original DA. MIDA outperformed the original DA by 91.33% for CEC2014 benchmarks and 94.25% for CEC2020 benchmarks. In addition, MIDA came first in comparison with twelve different SOTAs from the literature. In comparisons with different DA versions, MIDA took the second place in terms of statistical performance. Computational complexity was examined in the CEC2020 benchmarks and it was seen that MIDA has a more effective run time than DA. Finally, the short-term wind speed forecasting results of the ANFIS-DA hybrid model according to four different error metrics are more successful than those of the ANFIS-DA hybrid model.

A data-driven physics-informed stochastic framework for hurricane-induced risk estimation of transmission tower-line systems under a changing climate

Hurricanes are one of the most devastating natural hazards which result in significant damage to civil infrastructure. The transmission tower-line system is specifically highly vulnerable to hurricane effects. Recent studies have indicated that the collapse of the transmission tower-line system during hurricane events is mainly due to the joint occurrence of wind and rain. Therefore, it is important to assess the failure probabilities of these structures under wind and rain-induced loads. With climate change, those probabilities are expected to significantly change where the brunt of the damage and economic losses would be disproportionately borne by coastal communities. This study proposes a hybrid risk-responsive, data-driven and physics-informed framework which evaluates the hurricane-induced damage to transmission lines in vulnerable regions and communities under several climate scenarios. The framework is founded upon an advanced physics informed hurricane simulation model followed by a Gaussian kernel density technique to efficiently estimate the intensity measures and quantify the associated uncertainties accurately. The generated hazard intensities are coupled with fragility surfaces expressed in terms of wind speed and rain intensity. Then, the failure probabilities are obtained for the transmission tower-line system under the joint excitation and future climate scenario. The framework is applied to several coastal cities along the US east coast. It is shown that, due to the involved system nonlinearity, the obtained failure probabilities change drastically with the future climate scenario. In addition, rainfall-induced loads which significantly impact the failure probabilities of the transmission tower-line systems are not only function of the selected sites, but also depend on the considered limit state and climate scenario. Therefore, their consideration is important for the structural design and estimation of the system performance.

Extended Fuzzy Rule Suram for Coffee Drying System

Extended fuzzy rule Suram is an algorithm developed to control a drying system using diesel as an energy source by modifying the value of the fuzzy membership function {0.5,1]. For a coffee drying room control system with solar energy, the bleak rule is based on fuzzy logic with variables of weather, air condition and wind speed. This algorithm is applied to the coffee drying process. The state variable membership function is represented in error values ​​and changes in error with a typical triangular and trapezoidal map for weather variables, air conditions, while wind speed is expressed in terms of wind speed. The results of the analysis of experiments with 16 fuzzy rules to control system output according to weather conditions obtained optimization of the use of solar energy by minimizing the use of electrical energy by heating. This algorithm also adjusts the coffee drying schedule by controlling the chamber, namely through temperature and humidity control. The results of the application of this algorithm show that the efficiency of electrical energy reaches 40,86%.

Implementation of the urban parameterization scheme in the Delhi model with an improved urban morphology

AbstractThe current study highlights the importance of a detailed representation of urban processes in numerical weather prediction models and emphasizes the need for accurate urban morphology data for improving the near‐surface weather prediction over Delhi, a tropical Indian city. The Met Office Reading Urban Surface Exchange Scheme (MORUSES), a two‐tile urban energy‐budget parameterization scheme, is introduced in a high‐resolution (330‐m) model of Delhi. A new empirical relationship is established for the MORUSES scheme from the local urban morphology of Delhi. The performance is evaluated using both the newly developed empirical relationships (MORUSES‐IND) and the existing empirical relationships for the MORUSES scheme (MORUSES‐LON) against the default one‐tile configuration (BEST‐1t) for clear and foggy events and validations are performed against ground observations. MORUSES‐IND exhibits a significant improvement in the diurnal evolution of the wind speed in terms of amplitude and phase, compared with the other two configurations. Screen temperature () simulations using MORUSES‐IND reduce the warm bias, especially during the evening and night hours. The root‐mean‐square error of is reduced up to 29% using MORUSES‐IND for both synoptic conditions. The diurnal cycle of surface‐energy fluxes is reproduced well using MORUSES‐IND. The net longwave fluxes are underestimated in the model and biases are more significant during foggy events, partly due to the misrepresentation of fog. An urban cool island (UCI) effect is observed in the early morning hours during clear‐sky conditions, but it is not evident on foggy days. Compared with BEST‐1t, MORUSES‐IND represents the impact of urbanization more realistically, which is reflected in the reduction of the urban heat island and UCI in both synoptic conditions. Future works would improve the coupling between the urban surface energy budget and anthropogenic aerosols by introducing MORUSES‐IND in a chemistry aerosol framework model.

Short term wind speed forecasting using time series techniques

ABSTRACT Wind power is a renewable energy source that can be used in place of conventional fossil-fuel-based power. Although the integration of wind power has many benefits, the conventional electrical system requires a constant supply, i.e. the power supply ought to be equivalent to the power demand consistently. It’s tough to keep this equilibrium because of the variation of the wind power output. Improving wind speed predictions is one of the solutions to the balance problem. This paper centers around short-term wind speed forecasting using time series methods. A time series is a logically ordered succession of numerical data points that can be used to study any variable that changes over time. This paper applies a variety of time series forecasting techniques – Exponential Smoothing, ARIMA, LSTM, and a novel hybrid LSTM-ARIMA model – to three different time periods of hourly measured wind speed data. The performance of the models is compared using metrics such as MSE (Mean Squared Error), RMSE (Root Mean Squared Error), NSE (Nash – Sutcliffe model efficiency coefficient), MAE (Mean Absolute Error), and MAPE (Mean Absolute Percentage Error). The proposed LSTM-ARIMA model has the highest prediction accuracy and achieves the least error metrics at all time scales. It outperforms other architectures, achieving a MAPE of 24.78% for the 12-day scale, 9.30% for the 2-day scale, and 12.80% for the 1-day scale.

A one-year comparison of new wind atlases over the North Sea

The New European Wind Atlas (NEWA) and the Norwegian hindcast archive (NORA3) database have become publicly available since the end of 2019 and mid-2021, respectively. They aim to model the long-term wind climatology with a spatial resolution of ca. 3 km and a temporal resolution of 1 h (NORA3) or 30 min (NEWA). Both products have a high potential for wind energy applications. Although their geographical coverages partly overlap, an inter-comparison of the NEWA and NORA3 databases in an offshore environment is still lacking. The paper compares the hourly mean wind speed and wind direction recorded in 2009 at the FINO1 platform (North Sea) with hindcast data from the NEWA and the NORA3 database. Both products were found to provide reliable estimates of the mean wind speed at 101 m above sea level. However, NORA3 shows slightly better performances than NEWA for the mean wind speed in terms of root-mean-square error, bias, earth mover’s distance (EMD) and Pearson correlation coefficient. For the mean wind direction, a larger circular EMD than previously documented is found, which could be due to a directional bias in the wind vane data. Finally, the Brunt-Väisälä frequency is computed using sea-surface temperature analyses and the air temperature from NORA3 and NEWA at 101 m above sea level. The encouraging description of the static atmospheric stability by the wind atlases opens the possibility to study in more detail thermally-induced wind events for wind resource assessment or wind turbine design.

Serial-parallel dynamic echo state network: A hybrid dynamic model based on a chaotic coyote optimization algorithm for wind speed prediction

Multiple reservoirs have been widely used in nonlinear time series forecasting. The hybrid approach is recognized as the mainstream forecasting method in complex wind speed prediction systems. However, application research and error correction of the multiple reservoirs hybrid model in the wind energy domain are still insufficient. Therefore, a serial-parallel dynamic echo state network (SP-ESN) with dual dynamic characteristics is proposed firstly. Its serial structure can capture the short-term memory information of the sequence and dynamically select the length of the training set, while the parallel structure has the ability to automatically correct the prediction error of the serial structure. Then, a dynamic prediction model based on phase space reconstruction that includes the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method, the SP-ESN and the chaotic coyote optimization algorithm (CCOA) is designed in this paper. After data preprocessing of the original sequence by ICEEMDAN, the generated decomposed subsequence is input into the SP-ESN model for prediction and the final forecasting value is obtained accordingly. The CCOA is used to optimize the developed SP-ESN model parameters. Five wind speed datasets from Ningxia Hui Autonomous Region and Qinghai Province of China are selected for experiments. Six point prediction evaluation metric, forecasting efficiency and Diebold-Mariano test are used to evaluate the performance of the proposed model in ultra-short term wind speed forecasting. The case study results show that the proposed hybrid model achieves satisfactory performance compared with 5 datasets and 14 models, with mean absolute percentage error (MAPE) of 0.8405%, 1.2207%, 0.5163% 0.9791% and 0.3826%, respectively.

Evaluation of CORDEX-SEA Models on Wind Simulation during the Southwest Monsoon in the Southwestern Part of the South China Sea

This paper assesses the reliability of regional climate downscaling products from the Coordinated Regional Downscaling Experiment-Southeast Asia (CORDEX-SEA) in reproducing the present-day wind at the East Coast of Peninsular Malaysia (ECPM). A total of five CORDEX-SEA products, namely CNRM-CM5(RegCM4), CSIRO-Mk3-6-0(RegCM4), EC-EARTH(RegCM4), HadGEM2-ES(RegCM4) and MPI-ESM-MR(RegCM4), with a resolution of 25 km, were compared to reference data from the European Centre Medium-Range Weather Forecasts (ECMWF) fifth generation atmospheric reanalysis ERA5 spanning 1980–2005. This study focused on the period of the southwest monsoon, specifically the months of June, July and August (JJA), as wind during this season is known to play an important role in generating upwelling at the ECPM. The results obtained show that only three of the CORDEX-SEA products, CSIRO-Mk3-6-0(RegCM4), HadGEM2-ES(RegCM4) and MPI-ESM-MR(RegCM4), were able to reproduce the wind in terms of wind speed, wind stress curl and wind pattern as effectively as ERA5. An ensemble was made from these three CORDEX-SEA products, and it was found that the ensemble was able to reproduce a value of the Upwelling Index (UI) that was similar to the reference data, albeit with some bias. Hence, only the three stated CORDEX-SEA products and their ensemble are suitable to be used for future climate change studies within the region.

A Ka-Band Wind Geophysical Model Function Using Doppler Scatterometer Measurements from the Air-Sea Interaction Tower Experiment

Physical understanding and modeling of Ka-band ocean surface backscatter is challenging due to a lack of measurements. In the framework of the NASA Earth Ventures Suborbital-3 Submesoscale Ocean Dynamics Experiment (S-MODE) mission, a Ka-Band Ocean continuous wave Doppler Scatterometer (KaBODS) built by the University of Massachusetts, Amherst (UMass) was installed on the Woods Hole Oceanographic Institution (WHOI) Air-Sea Interaction Tower. Together with ASIT anemometers, a new data set of Ka-band ocean surface backscatter measurements along with surface wind/wave and weather parameters was collected. In this work, we present the KaBODS instrument and an empirical Ka-band wind Geophysical Model Function (GMF), the so-called ASIT GMF, based on the KaBODS data collected over a period of three months, from October 2019 to January 2020, for incidence angles ranging between 40° and 68°. The ASIT GMF results are compared with an existing Ka-band wind GMF developed from data collected during a tower experiment conducted over the Black Sea. The two GMFs show differences in terms of wind speed and wind direction sensitivity. However, they are consistent in the values of the standard deviation of the model residuals. This suggests an intrinsic geophysical variability characterizing the Ka-band surface backscatter. The observed variability does not significantly change when filtering out swell-dominated data, indicating that the long-wave induced backscatter modulation is not the primary source of the KaBODS backscatter variability. We observe evidence of wave breaking events, which increase the skewness of the backscatter distribution in linear space, consistent with previous studies. Interestingly, a better agreement is seen between the GMFs and the actual data at an incidence angle of 60° for both GMFs, and the statistical analysis of the model residuals shows a reduced backscatter variability at this incidence angle. This study shows that the ASIT data set is a valuable reference for studies of Ka-band backscatter. Further investigations are on-going to fully characterize the observed variability and its implication in the wind GMF development.

A Deep Attention Convolutional Recurrent Network Assisted by K-Shape Clustering and Enhanced Memory for Short Term Wind Speed Predictions

Due to the increasing penetration of wind energy in nowadays power grids, the accurate wind speed prediction (WSP) is critical to more efficient and reliable operations of power systems containing wind turbines. This paper presents a novel deep attention convolutional recurrent network with K-Shape and enhanced memory (DACRN-KM) for more accurate short-term WSP. To well capture spatial-temporal information among wind speeds measured across the wind farm, a DACRN is firstly developed to extract latent representations. Next, an auto-updated memory module is developed to rebuild latent representations based on historical records. A K-shape clustering algorithm is applied to derive K patterns of rebuilt latent representations and the final prediction layer (FPL) is developed to generate the WSP result for latent representations assigned to one out of K patterns each time. The effectiveness of the proposed DACRN-KM is validated with data collected from 25 wind turbines (WTs) in multiple wind farms, in which a part of them is publicly accessible, by benchmarking against a set of the commonly considered and recently reported methods.

Investigating Wind Energy Potential in Tahiti, French Polynesia

In order to achieve France’s goal of carbon neutrality by 2050, the French Polynesian administration has set the objective of producing 100% of the local electricity requirements from renewable energy resources. To this end, we present the wind characteristics at six selected locations in Tahiti. Surface wind observations from 2008 to 2020 obtained from the Meteorological Service of French Polynesia are analysed in terms of wind speed, dominant wind direction and power density to identify the most suitable locations for the deployment of wind farms. The Weibull distribution is used to fit the wind speed data recorded at 10 m above ground level, as it is widely used by turbine manufacturers. Then, wind speed is extrapolated vertically up to the hub height with the power law, which is also commonly used in wind energy studies. The theoretical annual energy output and capacity factor of four selected commercial wind turbines are assessed for each site in order to provide stakeholders with the relevant information regarding wind energy harvesting in Tahiti. Power law indices lower than 0.2 were chosen. Our results show that all year round, two sites, Faaa and Tautira, are suitable to host wind turbines, even with a power law index as low as 0.1.

Monitoring of Climatic Factors Affecting on Dust & Sand Storm in Tehran Province

In this study, according to the objectives and implementation method, climatic factors affecting ‎the dust phenomenon in active meteorological stations in Tehran province for a period of 30 years ‎were studied and analyzed. The results showed that in Mehrabad meteorological station, ‎temperature, humidity, rainfall, evaporation factors were directly related to DSI indices, wind ‎trend and dust storm changes, Lancaster, wind and storm diagrams, SPEI and the increase of ‎drought indices in the statistical period increased with temperature, evaporation and decrease. ‎Rainfall and humidity are proportional. DSI dust storm indexes were compared to 1 and ‎Lancaster drought index was compared to 50 and SPEI index was compared positively and ‎negatively in different years of the statistical period in the region and was classified in terms of ‎drought and wet season. The results of monthly and annual changes in wind trend and dust ‎storms showed that the region has no wind in all months and seasons with a speed of more than ‎‎20 meters per second. In terms of wind speed and frequency percentage, the highest wind speed ‎was related to late May and early June, and the lowest wind speed was related to late December ‎and early January. In addition, according to the changes in the wind trend and the documents of the ‎winds and thunderstorms, the prevailing wind direction in the region was from the west.‎

Offshore wind resource assessment using reanalysis data

The growth rate of offshore wind is increasing due to technological advancement and reduction in cost. An approach using mast measured data at coastline and reanalysis data is proposed for offshore wind resource assessment, especially for developing countries. The evaluation of fifth generation European Reanalysis (ERA5) data was performed against measured data using statistical analysis. ERA5 data slightly underestimates wind speed and wind direction with percentage bias of less than 1%. Wind resource assessment of region in Exclusive Economic Zone (EEZ) of Pakistan was performed in terms of wind speed and Wind Power Density (WPD). The range of monthly mean wind speed and WPD in the region was 4.03–8.67 m/second and 73–515 W/m2 respectively. Most-probable wind speed and dominating wind direction on corners and center of the region were found using probability distributions and wind rose diagrams respectively. Most-probable wind speed ranges 4.41–7.64 m/second and dominating wind direction is southwest.