scholarly journals Wind energy potential assessment based on wind speed, its direction and power data

2021 ◽  
Author(s):  
Zhiming Wang ◽  
Weimin Liu
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Direction ◽  
Mean Squared Error ◽  
Mixture Distribution ◽  
Information Criterion ◽  
Energy Potential ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Wind Energy Potential

Abstract Based on wind speed, direction and power data, an assessment method of wind energy potential using finite mixture statistical distributions is proposed. Considering the correlation existing and the effect between wind speed and direction, the angular-linear modeling approach is adopted to construct the joint probability density function of wind speed and direction. For modeling the distribution of wind power density and estimating model parameters, based on expectation-maximization algorithm, a two-component three-parameter Weibull mixture distribution is chosen as wind speed model, and a von Mises mixture distribution with nine components and six components are selected as wind direction and relation circular variable models, respectively. A comprehensive technique of model selection, which includes Akaike information criterion, Bayesian information criterion, the coefficient of determination R2 and root mean squared error, is used to select the optimal model in all candidate models. The proposed method is applied to averaged 10-minute field monitoring wind data and compared with the other estimation methods and judged by the values of R2 and root mean squared error, histogram plot and wind rose diagram. The results show that the proposed method is effective and the area under study is not suitable for wide wind turbine applications, and the estimated wind energy potential would be inaccuracy without considering the influence of wind direction.

scholarly journals Wind energy potential assessment based on wind speed, its direction and power data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhiming Wang ◽  
Weimin Liu
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Direction ◽  
Mean Squared Error ◽  
Mixture Distribution ◽  
Information Criterion ◽  
Energy Potential ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Wind Energy Potential

AbstractBased on wind speed, direction and power data, an assessment method of wind energy potential using finite mixture statistical distributions is proposed. Considering the correlation existing and the effect between wind speed and direction, the angular-linear modeling approach is adopted to construct the joint probability density function of wind speed and direction. For modeling the distribution of wind power density and estimating model parameters of null or low wind speed and multimodal wind speed data, based on expectation–maximization algorithm, a two-component three-parameter Weibull mixture distribution is chosen as wind speed model, and a von Mises mixture distribution with nine components and six components are selected as the models of wind direction and the correlation circular variable between wind speed and direction, respectively. A comprehensive technique of model selection, which includes Akaike information criterion, Bayesian information criterion, the coefficient of determination R2 and root mean squared error, is used to select the optimal model in all candidate models. The proposed method is applied to averaged 10-min field monitoring wind data and compared with the other estimation methods and judged by the values of R2 and root mean squared error, histogram plot and wind rose diagram. The results show that the proposed method is effective and the area under study is not suitable for wide wind turbine applications, and the estimated wind energy potential would be inaccuracy without considering the influence of wind direction.

scholarly journals Assessment of Offshore Wind Characteristics and Wind Energy Potential in Bohai Bay, China

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2879 ◽  
Author(s):  
Jianxing Yu ◽  
Yiqin Fu ◽  
Yang Yu ◽  
Shibo Wu ◽  
Yuanda Wu ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Direction ◽  
Resource Assessment ◽  
Offshore Wind ◽  
Bohai Bay ◽  
Energy Potential ◽  
Wind Potential ◽  
Wind Energy Potential ◽  
Wind Characteristics

Wind energy, one of the most sustainable renewable energy sources, has been extensively developed worldwide. However, owing to the strong regional and seasonal differences, it is necessary to first evaluate wind energy resources in detail. In this study, the offshore wind characteristics and wind energy potential of Bohai Bay (38.7° N, 118.7° E), China, were statistically analyzed using two-year offshore wind data with a time interval of one second. Furthermore, Nakagami and Rician distributions were used for wind energy resource assessment. The results show that the main wind direction in Bohai Bay is from the east (−15°–45°), with a speed below 12 m/s, mainly ranging from 4 to 8 m/s. The main wind speed ranges in April and October are higher than those in August and December. The night wind speed is generally higher than that in the daytime. The Nakagami and Rician distributions performed reasonably in calculating the wind speed distributions and potential assessments. However, Nakagami distribution provided better wind resource assessment in this region. The wind potential assessment results suggest that Bohai Bay could be considered as a wind class I region, with east as the dominant wind direction.

Parametric study for the prediction of wind energy potential over the southern part of India using neural network and geographic information system approach

2019 ◽  
Vol 234 (1) ◽  
pp. 96-109 ◽  
Author(s):  
Khalid Anwar ◽  
Sandip Deshmukh
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Meteorological Parameters ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Energy Potential ◽  
Squared Error ◽  
Wind Potential ◽  
Wind Energy Potential ◽  
Hidden Layer

Wind energy potential in India has so far not been evaluated state wise. Moreover, the prediction and assessment of wind potential are difficult due to the complexity of its nature. Here, a parametric study is done for the better prediction of wind potential using generalized feed-forward with back-propagation neural networks. Effect of three meteorological parameters (pressure, relative humidity, and temperature) on the wind speed prediction is studied in southern states of India. The meteorological parameters taken here are monthly mean, measured at ground station. These data were obtained at 28 sites over a period of 20 years from the IMD, Pune. Three different architectures of artificial neural network model were designed, trained, and evaluated for the prediction of wind speed. All three models have been optimized for varying neurons in the hidden layer. To evaluate the developed artificial neural network model for test locations, mean absolute percentage error and mean squared error have been calculated. It was found that the model with relative humidity as input parameter and having six neurons in the hidden layer give better prediction of the wind speed. The correlation coefficients were higher than 0.96 and the mean absolute percentage error and mean squared error of all test locations is less than 2.5 and 0.0176, respectively, which show high reliability of the model for the prediction of the wind speed within the region of study. Predicted wind speed has been analyzed and used to create monthly mean maps using geographic information system technology.

Calculation of Weibull Distribution parameters at low wind speed and performance analysis

2021 ◽  
pp. 1-24
Author(s):  
Yusuf Alper Kaplan
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Weibull Distribution ◽  
Energy Potential ◽  
Average Wind Speed ◽  
Statistical Indicator ◽  
Low Wind Speed ◽  
Distribution Parameters ◽  
Wind Energy Potential ◽  
And Performance

In this study, the compatibility of the real wind energy potential to the estimated wind energy potential by Weibull Distribution Function (WDF) of a region with low average wind speed potential was examined. The main purpose of this study is to examine the performance of six different methods used to find the coefficients of the WDF and to determine the best performing method for selected region. In this study seven-year hourly wind speed data obtained from the general directorate of meteorology of this region was used. The root mean square error (RMSE) statistical indicator was used to compare the efficiency of all used methods. Another main purpose of this study is to observe the how the performance of the used methods changes over the years. The obtained results showed that the performances of the used methods showed slight changes over the years, but when evaluated in general, it was observed that all method showed acceptable performance. Based on the obtained results, when the seven-year data is evaluated in this selected region, it can be said that the MM method shows the best performance.

scholarly journals Climate Change Induced Uncertainty of Wind Energy Potential for the Azov and Black Seas Coastal Zone

2020 ◽  
Author(s):  
V. P. Evstigneev ◽  
◽  
N. A. Lemeshko ◽  
V. A. Naumova ◽  
M. P. Evstigneev ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Black Sea ◽  
Operating Time ◽  
Energy Output ◽  
Energy Potential ◽  
Wind Energy Potential ◽  
Wind Climate

The paper deals with assessing an impact of wind climate change on the wind energy potential of the Azov and Black Sea coast region. A lower estimate of operating time for wind power installation and a potential annual energy output for the region are given for the case of Vestas V117-4.2MW. Calculation has been performed of a long-term mean wind speed for two adjacent climatic periods (1954–1983 and 1984–2013) based on data from meteorological stations of the Black and Azov Sea region. The results show a decrease in wind speed at all meteorological stations except for Novorossiysk. The wind climate change is confirmed by comparing two adjoined 30-year periods and by estimating linear trends of the mean annual wind speed for the period 1954–2013, which are negative and significant for almost all meteorological stations in the region (α = 1 %). The trend values were estimated by the nonparametric method of robust linear smoothing using the Theil – Sen function. In the present study, the uncertainty of wind energy resource induced by a gradual wind climate change is estimated for perspective planning of this branch of energy sector. Despite the observed trends in the wind regime, average wind speeds in the Azov and Black Sea region are sufficient for planning the location of wind power plants.

scholarly journals Wind Power potential in Kigali and Western provinces of Rwanda

2015 ◽  
Vol 2 (1) ◽  
pp. 25-36
Author(s):  
Otieno Fredrick Onyango ◽  
Sibomana Gaston ◽  
Elie Kabende ◽  
Felix Nkunda ◽  
Jared Hera Ndeda
Keyword(s):  
Wind Speed ◽  
Probability Density ◽  
Wind Power ◽  
Wind Direction ◽  
Small Scale ◽  
Energy Potential ◽  
Wind Speeds ◽  
Mean Wind Speed ◽  
Wind Energy Potential ◽  
Low Wind Speeds

Wind speed and wind direction are the most important characteristics for assessing wind energy potential of a location using suitable probability density functions. In this investigation, a hybrid-Weibull probability density function was used to analyze data from Kigali, Gisenyi, and Kamembe stations. Kigali is located in the Eastern side of Rwanda while Gisenyi and Kamembe are to the West. On-site hourly wind speed and wind direction data for the year 2007 were analyzed using Matlab programmes. The annual mean wind speed for Kigali, Gisenyi, and Kamembe sites were determined as 2.36m/s, 2.95m/s and 2.97m/s respectively, while corresponding dominant wind directions for the stations were ,  and  respectively. The annual wind power density of Kigali was found to be  while the power densities for Gisenyi and Kamembe were determined as and . It is clear, the investigated regions are dominated by low wind speeds thus are suitable for small-scale wind power generation especially at Kamembe site.

Wind Energy Potential in Nine Coastal Sites in Malaysia

2013 ◽  
Vol 1 (1) ◽  
pp. 10-15
Author(s):  
Kamaruzzaman Sopian ◽  
Tamer Khatib
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Data Bank ◽  
Energy Potential ◽  
Average Wind Speed ◽  
Speed Distribution ◽  
Average Wind ◽  
Wind Speed Distribution ◽  
Wind Energy Potential ◽  
Swept Area

 In this paper, the wind energy potential in Malaysia is examined by analyzing hourly wind speed data for nine coastal sites namely Bintulu, Kota Kinabalu, Kuala Terengganu, Kuching, Kudat, Mersing, Sandakan, Tawau and Pulau Langkawi. The monthly averages of wind speed and wind energy are calculated. Moreover, the wind speed distribution histogram is constructed for these sites. The results showed that the average wind speed for these sites is in the range of (1.8-2.9) m/s while the annual energy of the wind hitting a wind turbine with a 1 m2 swept area is in the range of (15.4-25.2) kWh/m2.annum. This paper provides a data bank for wind energy for Malaysia.

scholarly journals Assessment of wind energy potential across varying topographical features of Tamil Nadu, India

2019 ◽  
Vol 38 (1) ◽  
pp. 175-200 ◽  
Author(s):  
Shafiqur Rehman ◽  
Narayanan Natarajan ◽  
Mangottiri Vasudevan ◽  
Luai M Alhems
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Tamil Nadu ◽  
High Energy ◽  
Energy Potential ◽  
Annual Average ◽  
Wind Power Density ◽  
Wind Energy Potential

Wind energy is one of the abundant, cheap and fast-growing renewable energy sources whose intensive extraction potential is still in immature stage in India. This study aims at the determination and evaluation of wind energy potential of three cities located at different elevations in the state of Tamil Nadu, India. The historical records of wind speed, direction, temperature and pressure were collected for three South Indian cities, namely Chennai, Erode and Coimbatore over a period of 38 years (1980-2017). The mean wind power density was observed to be highest at Chennai (129 W/m2) and lowest at Erode (76 W/m2) and the corresponding mean energy content was highest for Chennai (1129 kWh/m2/year) and lowest at Erode (666 kWh/m2/year). Considering the events of high energy-carrying winds at Chennai, Erode and Coimbatore, maximum wind power density were estimated to be 185 W/m2, 190 W/m2 and 234 W/m2, respectively. The annual average net energy yield and annual average net capacity factor were selected as the representative parameters for expressing strategic wind energy potential at geographically distinct locations having significant variation in wind speed distribution. Based on the analysis, Chennai is found to be the most suitable site for wind energy production followed by Coimbatore and Erode.

scholarly journals An Assessment of Wind Energy Potential for the Three Topographic Regions of Eritrea

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1846 ◽  
Author(s):  
Teklebrhan Negash ◽  
Erik Möllerström ◽  
Fredric Ottermo
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Coastal Region ◽  
Energy Potential ◽  
Wind Power Density ◽  
Prevailing Wind Direction ◽  
Central Highland ◽  
Wind Energy Potential

This paper presents the wind energy potential and wind characteristics for 25 wind sites in Eritrea, based on wind data from the years 2000–2005. The studied sites are distributed all over Eritrea, but can roughly be divided into three regions: coastal region, western lowlands, and central highlands. The coastal region sites have the highest potential for wind power. An uncertainty, due to extrapolating the wind speed from the 10-m measurements, should be noted. The year to year variations are typically small and, for the sites deemed as suitable for wind power, the seasonal variations are most prominent in the coastal region with a peak during the period November–March. Moreover, Weibull parameters, prevailing wind direction, and wind power density recalculated for 100 m above ground are presented for all 25 sites. Comparing the results to values from the web-based, large-scale dataset, the Global Wind Atlas (GWA), both mean wind speed and wind power density are typically higher for the measurements. The difference is especially large for the more complex-terrain central highland sites where GWA results are also likely to be more uncertain. The result of this study can be used to make preliminary assessments on possible power production potential at the given sites.

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