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The objective of this study is to perform an analysis to determine the most suitable type of wind turbine that can be installed at a specific location for electricity generation, using annual measurements of wind characteristics and meteorological parameters. Wind potential analysis has shown that the analyzed location is suitable for the development of a wind farm. The analysis was carried out for six different types of wind turbines, with a power ranging from 1.5 to 3.0 MW and a hub height set at 80 m. Wind power potential was assessed using the Weibull analysis. The values of the scale coefficient c were determined, and a large monthly variation was observed, with values ranging from 1.92 to 8.36 m/s and an annual value of 4.95 m/s. Monthly values for the shape coefficient k varied between 0.86 and 1.53, with an annual value of 1.07. Additionally, the capacity factor of the turbines was determined, ranging from 17.75 to 22.22%. The Vestas turbine, with a nominal power of 2 MW and a capacity factor of 22.22%, proved to be the most efficient wind turbine for the specific conditions of the location. The quantity of greenhouse gas emissions that will be reduced if this type of turbine is implemented was also calculated, considering the average CO 2 emission intensity factor (kg CO 2 /kWh) of the national electricity system.

The spatiotemporal characteristics of the near‐surface wind speed (NWS), wind speed at 100 m hub height (HWS), and wind power density (WPD) over China are assessed during 1980–2021. A homogenization process is applied to NWS at 292 basic meteorological stations. A total of 336 breakpoints are recognized, with 122 associated with instrument replacement, 113 attributed to station relocation, and 101 due to unrecorded reasons. The homogenization method does not alter the spatial patterns or seasonal variations of NWS, but it does boost the mean NWS over China annually and seasonally, while also strengthening the long‐term decreasing trends. As for the temporal standard deviation (STD) for NWS, high values are primarily found over Inner Mongolia and Northeast China, with the seasonal maximum occurring in spring. After homogenization, the STD of NWS over China is reduced annually and seasonally, and the long‐term decreasing trends are somewhat weaker. The results for HWS are comparable to those for NWS. Notably, the lower mean state and weaker fluctuation of wind speed in recent years have two opposing implications for wind power production. Similar to the NWS mean state, the annual mean WPD over China is largely increased after homogenization with a faster decreasing trend.

Currently, Pakistan is facing severe energy crises and global warming effects. Hence, there is an urgent need to utilize renewable energy generation. In this context, Pakistan possesses massive wind energy potential across the coastal areas. This paper investigates and numerically analyzes coastal areas’ wind power density potential. Eight different state-of-the-art numerical methods, namely an (a) empirical method, (b) graphical method, (c) wasp algorithm, (d) energy pattern method, (e) moment method, (f) maximum likelihood method, (g) energy trend method, and (h) least-squares regression method, were analyzed to calculate Weibull parameters. We computed Weibull shape parameters (WSP) and Weibull scale parameters (WCP) for four regions: Jiwani, Gwadar, Pasni, and Ormara in Pakistan. These Weibull parameters from the above-mentioned numerical methods were analyzed and compared to find an optimal numerical method for the coastal areas of Pakistan. Further, the following statistical indicators were used to compare the efficiency of the above numerical methods: (i) analysis of variance (R2), (ii) chi-square (X2), and (iii) root mean square error (RMSE). The performance validation showed that the energy trend and graphical method provided weak performance for the observed period for four coastal regions of Pakistan. Further, we observed that Ormara is the best and Jiwani is the worst area for wind power generation using comparative analyses for actual and estimated data of wind power density from four regions of Pakistan.

The South China Sea is a major shipping hub between the West Pacific and Indian Oceans. In this region, the demand for energy is enormous, both for residents’ daily lives and for economic development. Wave energy and wind energy are two major clean and low-cost ocean sources of renewable energy. The reasonable development and utilization of these energy sources can provide a stable energy supply for coastal cities and remote islands of China. Before wave energy and wind energy development, however, we must assess the potential of each of these sources. Based on high-resolution and high-accuracy wave field data and wind field data obtained by ERA-Interim reanalysis for the recent 38-year period from 1979–2016, the joint development potential of wave energy and wind energy was assessed in detail for offshore and nearshore areas in the South China Sea. Based on potential installed capacity, the results revealed three promising areas for the joint development of nearshore wave energy and wind energy, including the Taiwan Strait, Luzon Strait and the sea southeast of the Indo-China Peninsula. For these three dominant areas (key stations), the directionality of wave energy and wind energy propagation were good in various seasons; the dominant wave conditions and the dominant wind conditions were the same, which is advantageous for the joint development of wave and wind energy. Existing well-known wave energy converters (WECs) are not suitable for wave energy development in the areas of interest. Therefore, we must consider the distributions of wave conditions and develop more suitable WECs for these areas. The economic and environmental benefits of the joint development of wave and wind energy are high in these promising areas. The results described in this paper can provide references for the joint development of wave and wind energy in the South China Sea.

In the quest to scientifically develop power systems increasingly reliant on renewable energy sources, the potential and temporal complementarity of wind and solar power in China’s northwestern provinces necessitated a systematic assessment. Using ERA5 reanalysis data for wind speed and solar irradiance, an evaluation was carried out to determine the potential and spatial distribution of wind and solar power across these provinces. Land use types and terrestrial surface slopes were considered in gauging this potential. Theoretical wind and solar power outputs were then compared to understand their complementarity on annual, monthly, and hourly temporal scales. This exploration utilized methodologies including rank correlation coefficients, crossover frequency analysis, and standard deviation complementarity rates. Areas such as the Tarim Basin, Jungar Basin, and the northeastern part of Xinjiang, northwestern Qinghai, and northern Gansu were identified as having significant wind and solar power potential, with wind power densities reaching as high as 600 W/m2 and solar irradiance surpassing 2000 kWh/m2. In these energy-rich areas, the distinct complementarity between theoretical wind and solar outputs was discerned. On an annual scale, the complementarity appeared weakest, with only 7.48% of the combined provinces’ area showing medium-level complementarity. On a monthly scale, conversely, a pronounced complementarity was displayed, especially during the March–May and October–November periods. When evaluated on an hourly basis, an impressive 63.63% of the total output duration exhibited complementary characteristics.

This paper analyzes the wind energy parameters for different areas with low wind energy potential using the data from the global online wind map. The performance characteristics of different types of wind turbines are reviewed. The generated theoretical electric energy within one year for 9 types of wind turbines is presented. Suitable wind turbines for harnessing low wind potential, that can cover the electrical needs of one household are proposed.

Wind energy is becoming one of the most important elements toward the advancement of sustainable energy systems globally. The assessment of wind energy potential is critical to the optimization of resource application and improvement of technologies. This study focuses on fitting fourteen probability density functions (PDFs) to hourly wind speed data collected from six coastal cities of Pakistan: Gwadar, Jiwani, Karachi, Keti Bandar, Ormara, and Pasni, for the year 2023, measured at 10 m and 50 m heights. These selected distributions are Weibull, Rayleigh, Lognormal, Gamma, Normal, Generalized Extreme Value, Logistic, Nakagami, t Location-Scale, Extreme Value, Inverse Gaussian, Chi-Square, Pearson Type III, and Rician. Four goodness-of-fit (GoF) indices are employed to evaluate the performance of these distributions: root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R 2 ), and chi-squared (χ 2 ). These metrics give a clear report on each distribution’s aptness to emulate the wind speed information. Observed and computed wind power density (WPD) values are also determined to investigate the application of fitted distribution functions for practical purposes. The inspection of the simulation results shows that GEV, Weibull, Nakagami, and Gamma PDFs proved to be the most promising PDFs for describing wind speed data at 10 m, whereas GEV (predominantly), Weibull, Normal, and Logistic PDFs for wind speed data at 50 m. Further investigation revealed that the GEV distribution consistently exhibited better fitting characteristics, followed closely by Weibull, Nakagami, and Gamma distributions, making them highly suitable for characterizing the wind speed and determining wind energy potential. The extensively used Weibull distribution is not always the first choice. Consequently, the results presented in the paper provide fundamental information about the usage of the resource and energy production for Pakistani coastal wind sites.

Accurate estimation of wind power potential is important for resource assessment to install wind turbine. Weibull distribution functions (WDF) have been widely used and it is a function of wind speed (WS). With Turbine hub height WS get changes and it form complex nonlinear equations with WDF. To compute this paper introduces an innovative Transformer Neural Network (TNN) model for WDE estimation leverage self attention mechanism to capture complex pattern. For this wind power potential (WPP) of a site located in the Northeastern India is selected. The novelty is that WPP is performed up to 80 m height and not up to a 150 m height for North-Eastern India. Cubic Factor (CF) method is used for the evaluation of Weibull parameters, i.e., scale ‘c’ and shape ‘k’. CF and k are independent of height and are found to be 1.96 and 1.95, respectively. The scale varies from 2.65 to 3.90 from height 10 m to 150 m. TNN performance is evaluated by Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-squared (R²), Mean Bias Error (MBE), and Mean Absolute Percentage Error (MAPE). For the Cumulative Distribution Function (CDF), the model achieved an MSE of 0.0012, RMSE of 0.0357, MAE of 0.0238, R² of 0.9170, MBE of -0.0071, and MAPE of 12.3158%. In comparison, the Weibull density functions (WDF) estimation yielded an MSE of 0.0003, RMSE of 0.0178, MAE of 0.0130, R² of 0.9039, MBE of -0.00018, and MAPE of 11.67%. The results demonstrate the Transformer model’s high accuracy and robustness in estimating WDF, making it a reliable tool for assessing wind energy potential at different turbine hub heights.

Offshore wind power resources in the Red Sea waters of Saudi Arabia are yet to be explored. The objective of the present study is to assess offshore wind power resources at 49 locations in the Saudi waters of the Red Sea and prioritize the sites based on wind characteristics. To accomplish the set objective, long-term hourly mean wind speed (WS) and wind direction (WD) at 100 m above mean sea level, temperature, and pressure data near the surface were used at sites L1-L49 over 43 years from 1979 to 2021. The long-term mean WS and wind power density (WPD) varied between 3.83 m/s and 66.6 W/m2, and 6.39 m/s and 280.9 W/m2 corresponding to sites L44 and L8. However, higher magnitudes of WS >5 m/s were observed at 34 sites and WPD of > 200 W/m2 at 21 sites. In general, WS, WPD, annual energy yield, mean windy site identifier, plant capacity factor, etc. were found to be increasing from east to west and from south to north. Similarly, the mean wind variability index and cost of energy were observed to be decreasing as one moves from east to west and south to north in the Saudi waters of the Red Sea.

Wind energy is the second largest source of renewable energy, across the world. For designing and construction of wind farms, most critical information is consistent wind resource assessment forecasts and appropriate models of wind speed distribution for a particular site. The purpose of this study is to provide the wind characteristics and wind potential evaluation of offshore locations, in Gujarat in India, using the wind Weibull density function. The Weibull shape k and scale c parameters are computed using six distinct numerical approaches at two different heights, to determine wind power density. The LiDAR sensor was used to capture the time series wind data. The goodness of fit test, which includes the RMSE, R 2 , MAPE, and χ 2 is considered to evaluate the performance of the selected methods. Wind power densities are calculated from the acquired results with the help of estimated parameter values, all the methods used in this study were found to be appropriate for Weibull distribution parameters estimation. The MLM has been determined to offer the most accurate evaluation of wind potential. The WPD computed from observed wind data was compared to the obtained power densities of the specified region. The evaluated data is a considered as preliminary characteristic of wind potential that aids in the wind energy conversion and determining the actual wind potential of a specific site.