scholarly journals Horizontal Extrapolation of Wind Speed Distribution Using Neural Network for Wind Resource Assessment

2017 ◽  
Vol 6 (12) ◽  
pp. 1498-1504
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Resource Assessment ◽  
Wind Resource Assessment ◽  
Speed Distribution ◽  
Wind Speed Distribution ◽  
Wind Resource

On the wind resource in Algeria: Probability distributions evaluation

2020 ◽  
pp. 095765092097588
Author(s):  
Houdayfa Ounis ◽  
Nawel Aries
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Probability Distributions ◽  
Resource Assessment ◽  
Descriptive Statistics ◽  
The Other ◽  
Wind Speeds ◽  
Wind Resource Assessment ◽  
The Mean ◽  
Wind Resource

The present study aims to present a contribution to the wind resource assessment in Algeria using ERA-Interim reanalysis. Firstly, the ERA-Interim reanalysis 10 m wind speed data are considered for the elaboration of the mean annual 10 m wind speed map for a period starting from 01-01-2000 to 31-12-2017. Moreover, the present study intends to highlight the importance of the descriptive statistics other than the mean in wind resource assessment. On the other hand, this study aims also to select the proper probability distribution for the wind resource assessment in Algeria. Therefore, nine probability distributions were considered, namely: Weibull, Gamma, Inverse Gaussian, Log Normal, Gumbel, Generalized Extreme Value (GEV), Nakagami, Generalized Logistic and Pearson III. Furthermore, in combination with the distribution, three parameter estimation methods were considered, namely, Method of Moment, Maximum Likelihood Method and L-Moment Method. The study showed that Algeria has several wind behaviours due to the diversified topographic, geographic and climatic properties. Moreover, the annual mean 10 m wind speed map showed that the wind speed varies from 2.3 to 5.3 m/s, where 73% of the wind speeds are above 3 m/s. The map also showed that the Algerian Sahara is windiest region, while, the northern fringe envelopes the lowest wind speeds. In addition, it has been shown that the study of the mean wind speeds for the evaluation of the wind potential alone is not enough, and other descriptive statistics must be considered. On the other hand, among the nine considered distribution, it appears that the GEV is the most appropriate probability distribution. Whereas, the Weibull distribution showed its performance only in regions with high wind speeds, which, implies that this probability distribution should not be generalized in the study of the wind speed in Algeria.

Wind Resource Assessment for the Kingdom of Bahrain

2008 ◽  
Vol 32 (5) ◽  
pp. 439-448 ◽  
Author(s):  
Hanan Al Buflasa ◽  
David Infield ◽  
Simon Watson ◽  
Murray Thomson
Keyword(s):  
Wind Speed ◽  
Energy Production ◽  
Resource Assessment ◽  
Measured Data ◽  
Shape Parameters ◽  
Main Island ◽  
Wind Resource Assessment ◽  
Mean Wind Speed ◽  
Wind Resource ◽  
Hourly Basis

The geographical distribution of wind speed (the wind atlas) for the kingdom of Bahrain is presented, based on measured data and on calculations undertaken using WAsP,. The data used were recorded by the Meteorological Directorate at a weather station situated at Bahrain International Airport, taken on an hourly basis for a period of time extended for ten years. These data indicate an annual mean wind speed of 4.6 m/s at 10 m height and mean Weibull scale and shape parameters C and k of 5.2 m/s and 1.9 respectively. At a typical wind turbine hub height of sixty metres, these values are extrapolated to 6.9 m/s, 7.8 m/s and 1.8 respectively, which suggests that the area has a good wind resource. The wind atlas shows that several locations in the less populated central and southern regions of the main island of the archipelago of Bahrain are potentially suitable for wind energy production.

scholarly journals Wind Speed using Improved Mixture Weibull Distribution

2019 ◽  
Vol 8 (6) ◽  
pp. 1871-1874
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Weibull Distribution ◽  
Wind Farms ◽  
Resource Assessment ◽  
Estimation Of Parameters ◽  
Basic Properties ◽  
Wind Resource Assessment ◽  
Proposed Model ◽  
Wind Resource

Wind is random in nature both in space and in time. Several technologies are used in wind resource assessment (WRA).The appropriate probability distribution used to calculate the available wind speed at that particular location and the estimation of parameters is the essential part in installing wind farms. The improved mixture Weibull distribution is proposed model which is the mixture of two and three parameter Weibull distribution with parameters including scale, shape, location and weight component. The basic properties of the proposed model and estimation of parameters using various methods are discussed.

scholarly journals Prediction of Wind Speed Distribution Using Artificial Neural Network: The Case of Saudi Arabia

2019 ◽  
Vol 163 ◽  
pp. 41-48
Author(s):  
Tayeb Brahimi ◽  
Fatima Alhebshi ◽  
Heba Alnabilsi ◽  
Ahmed Bensenouci ◽  
Mumu Rahman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Saudi Arabia ◽  
Wind Speed ◽  
Speed Distribution ◽  
Wind Speed Distribution ◽  
Artificial Neural

scholarly journals Comparison of three probability distributions and techno-economic analysis of wind energy production along the coastal belt of Pakistan

2020 ◽  
pp. 014459872093158 ◽  
Author(s):  
Muhammad Sumair ◽  
Tauseef Aized ◽  
Syed Asad Raza Gardezi ◽  
Muhammad Mahmood Aslam Bhutta ◽  
Syed Muhammad Sohail Rehman ◽  
...  
Keyword(s):  
Wind Speed ◽  
Energy Production ◽  
Probability Distributions ◽  
Resource Assessment ◽  
Wind Data ◽  
Coefficient Of Determination ◽  
Resource Estimation ◽  
Wind Resource Assessment ◽  
Coastal Belt ◽  
Wind Resource

Continuous probability distributions have long been used to model the wind data. No single distribution can be declared accurate for all locations. Therefore, a comparison of different distributions before actual wind resource assessment should be carried out. Current work focuses on the application of three probability distributions, i.e. Weibull, Rayleigh, and lognormal for wind resource estimation at six sites along the coastal belt of Pakistan. Four years’ (2015–2018) wind data measured each 60-minutes at 50 m height for six locations were collected from Pakistan Meteorological Department. Comparison of these distributions was done based on coefficient of determination ( R2), root mean square error, and mean absolute percentage deviation. Comparison showed that Weibull distribution is the most accurate followed by lognormal and Rayleigh, respectively. Wind power density ( PD) was evaluated and it was found that Karachi has the highest wind speed and PD as 5.82 m/s and 162.69 W/m2, respectively, while Jiwani has the lowest wind speed and PD as 4.62 m/s and 76.76 W/m2, respectively. Furthermore, feasibility of annual energy production (AEP) was determined using six turbines. It was found that Vestas V42 shows the worst performance while Bonus 1300/62 is the best with respect to annual energy production and Bonus 600/44 is the most economical. Finally, sensitivity analysis was carried out.

scholarly journals An Overview of Wind Resource Assessments With Special Reference to The Emirate of Ajman, UAE

2021 ◽  
Vol 6 ◽  
pp. 32
Author(s):  
Kais Muhammed Fasel ◽  
Abdul Salam K. Darwish ◽  
Peter Farrell ◽  
Hussein Kazem
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Clean Energy ◽  
Resource Assessment ◽  
Site Specific ◽  
Onshore Wind ◽  
Wind Resource Assessment ◽  
Wind Potential ◽  
The Government ◽  
Wind Resource

The continuous increase in clean energy demand and reduced CO2 emissions in the UAE and specifically the Emirate of Ajman has put an extreme challenge to the Government. Ajman is one of the seven emirates constituting the United Arab Emirates (UAE). Ajman is located along the Arabian Gulf on its West and bordered by the Emirate of Sharjah on its North, South, and East. The government is taking huge steps in including sustainability principles and clean energy in all of its developments. Successful implementation of green architecture law decree No 10 of 2018 effectively is a sign of such an initiative. Renewable energy sources in this country have had two folds of interest in solar and wind. Recent research works supported the feasibility of using wind energy as an alternative clean source of energy. Site-specific and accurate wind speed information is the first step in the process of bankable wind potential and wind Atlas. This study has compared how wind speed and its distribution varies for similar offshore and onshore locations between two different mesoscale data sources. Also, discussed the main environmental characteristics of Ajman that would influence the implementation of a major wind energy project. In addition, the study made a brief critical overview of the major studies undertaken in the Middle East and North Africa (MENA) region on wind resource assessment. Finally, based on the results, the study makes conclusions, recommendations and a way forward for a bankable wind resources assessment in the Emirate of Ajman. This paper would alert the wind energy industry about the consequence of not considering the best error corrected site specific suitable wind resource data along with other environmental characteristics. The study results show that for offshore, there is 2.9 m/s and for Onshore 4.9 m/s variations in wind speed at the same location between ECMWF Reanalysis (ERA-5) and NASA Satellite data. Hence It is concluded that error corrected site-specific wind resource assessment is mandatory for assessing the available bankable wind potential since there are considerable variations in wind speed distributions between mesoscale data sets for similar locations. The study also identifies that the Emirate of Ajman has limited space for onshore wind farms; hence the offshore site seems to have good potential that can be utilised for energy generation. However, individual wind turbines can be installed for exploiting the available site-specific onshore wind energy. Finally, the study recommends a way forward for a comprehensive wind resource assessment to help the Emirate of Ajman form a sustainable wind power generation policy.

scholarly journals Applying Satellite Data Assimilation to Wind Simulation of Coastal Wind Farms in Guangdong, China

2020 ◽  
Vol 12 (6) ◽  
pp. 973
Author(s):  
Wenqing Xu ◽  
Like Ning ◽  
Yong Luo
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Satellite Data ◽  
Surface Wind ◽  
Wrf Model ◽  
Wind Farms ◽  
Resource Assessment ◽  
Near Surface ◽  
Wind Resource Assessment ◽  
Wind Resource

With the development of the wind power industry in China, accurate simulation of near-surface wind plays an important role in wind-resource assessment. Numerical weather prediction (NWP) models have been widely used to simulate the near-surface wind speed. By combining the Weather Research and Forecast (WRF) model with the Three-dimensional variation (3DVar) data assimilation system, our work applied satellite data assimilation to the wind resource assessment tasks of coastal wind farms in Guangdong, China. We compared the simulation results with wind speed observation data from seven wind observation towers in the Guangdong coastal area, and the results showed that satellite data assimilation with the WRF model can significantly reduce the root-mean-square error (RMSE) and improve the index of agreement (IA) and correlation coefficient (R). In different months and at different height layers (10, 50, and 70 m), the Root-Mean-Square Error (RMSE) can be reduced by a range of 0–0.8 m/s from 2.5–4 m/s of the original results, the IA can be increased by a range of 0–0.2 from 0.5–0.8 of the original results, and the R can be increased by a range of 0–0.3 from 0.2–0.7 of the original results. The results of the wind speed Weibull distribution show that, after data assimilation was used, the WRF model was able to simulate the distribution of wind speed more accurately. Based on the numerical simulation, our work proposes a combined wind resource evaluation approach of numerical modeling and data assimilation, which will benefit the wind power assessment of wind farms.

scholarly journals A new formulation for rotor equivalent wind speed for wind resource assessment and wind power forecasting

Wind Energy ◽  
2015 ◽  
Vol 19 (8) ◽  
pp. 1439-1452 ◽  
Author(s):  
Aditya Choukulkar ◽  
Yelena Pichugina ◽  
Christopher T. M. Clack ◽  
Ronald Calhoun ◽  
Robert Banta ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Resource Assessment ◽  
Wind Power Forecasting ◽  
Wind Resource Assessment ◽  
New Formulation ◽  
Wind Resource ◽  
Power Forecasting

scholarly journals An Assessment of Onshore and Offshore Wind Energy Potential in India Using Moth Flame Optimization

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3063 ◽  
Author(s):  
Krishnamoorthy R ◽  
Udhayakumar K ◽  
Kannadasan Raju ◽  
Rajvikram Madurai Elavarasan ◽  
Lucian Mihet-Popa
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Likelihood Method ◽  
Wind Speeds ◽  
Speed Distribution ◽  
Wind Speed Distribution ◽  
South West Monsoon ◽  
Wind Distribution ◽  
Wind Resource

Wind energy is one of the supremely renewable energy sources and has been widely established worldwide. Due to strong seasonal variations in the wind resource, accurate predictions of wind resource assessment and appropriate wind speed distribution models (for any location) are the significant facets for planning and commissioning wind farms. In this work, the wind characteristics and wind potential assessment of onshore, offshore, and nearshore locations of India—particularly Kayathar in Tamilnadu, the Gulf of Khambhat, and Jafrabad in Gujarat—are statistically analyzed with wind distribution methods. Further, the resource assessments are carried out using Weibull, Rayleigh, gamma, Nakagami, generalized extreme value (GEV), lognormal, inverse Gaussian, Rician, Birnbaum–Sandras, and Bimodal–Weibull distribution methods. Additionally, the advent of artificial intelligence and soft computing techniques with the moth flame optimization (MFO) method leads to superior results in solving complex problems and parameter estimations. The data analytics are carried out in the MATLAB platform, with in-house coding developed for MFO parameters estimated through optimization and other wind distribution parameters using the maximum likelihood method. The observed outcomes show that the MFO method performed well on parameter estimation. Correspondingly, wind power generation was shown to peak at the South West Monsoon periods from June to September, with mean wind speeds ranging from 9 to 12 m/s. Furthermore, the wind speed distribution method of mixed Weibull, Nakagami, and Rician methods performed well in calculating potential assessments for the targeted locations. Likewise, the Gulf of Khambhat (offshore) area has steady wind speeds ranging from 7 to 10 m/s with less turbulence intensity and the highest wind power density of 431 watts/m2. The proposed optimization method proves its potential for accurate assessment of Indian wind conditions in selected locations.

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