scholarly journals Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability

PLoS ONE ◽  
2018 ◽  
Vol 13 (4) ◽  
pp. e0193772 ◽  
Author(s):  
Monowar Hossain ◽  
Saad Mekhilef ◽  
Firdaus Afifi ◽  
Laith M. Halabi ◽  
Lanre Olatomiwa ◽  
...  
Keyword(s):  
Wind Power ◽  
Power Density ◽  
Wind Power Density ◽  
Density Prediction

Wind Energy Potential along the Onshore and Offshore Qatar

2020 ◽  
Author(s):  
Aboobacker Valliyil Mohammed ◽  
Ebrahim M.A.S. Al-Ansari ◽  
Shanas Puthuveetil Razak ◽  
Veerasingam Subramanian ◽  
Vethamony Ponnumony
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Time Series Data ◽  
Series Data ◽  
Energy Potential ◽  
Wind Power Density ◽  
Enso Events ◽  
Wind Energy Potential

Wind energy is one among the clean and renewable energy resources. The utilization of nonconventional energies over the conventional sources helps to reduce the carbon emissions significantly. The present study aims at investigating the wind energy potential at select coastal locations of Qatar using ERA5 winds. ERA5 is the updated reanalysis product of the European Centre for Medium-range Weather Forecasts (ECMWF), in which the scatterometer and in situ wind data are assimilated to improve the accuracy of predictions, thus the long-term and shortterm variabilities are reasonably well captured. Compared to the earlier studies, in this work, we have assessed the wind power at inland and offshore areas of Qatar, considering 40-year long (1979-2018) time series data with hourly ERA5 winds at 10-m height. The results show that there is no significant increase or decrease of wind power around Qatar in the last 40 years in most of the locations, while there is a slight decreasing trend in the offshore areas of Al Ruwais. This indicates that the average wind power is consistently available throughout the years. The links of climatic indices, especially the ENSO events with the wind climate of Qatar, are clearly evident in the long-term data. As obvious, the offshore regions of Qatar have relatively high wind power compared to the land areas. Among the selected locations, the highest annual mean wind power density is obtained in the offshore Al Ruwais (152 W/m2), followed by offshore Ras Laffan (134 W/m2) and land area of Al Khor (120 W/m2). The maximum wind power density varies between 1830 and 2120 W/m2 in the land areas, while it is between 1850 and 2410 W/m2 in the offshore areas of Qatar. The highest wind power is consistently available during the prevalence of shamal winds in winter (January-March) as well as summer (June).

scholarly journals Long-term offshore wind power prediction using spatiotemporal kriging: A case study in China’s Guangdong Province

2019 ◽  
Vol 38 (3) ◽  
pp. 703-722
Author(s):  
Hongda Hu ◽  
Zhiyong Hu ◽  
Kaiwen Zhong ◽  
Jianhui Xu ◽  
Pinghao Wu ◽  
...  
Keyword(s):  
Wind Power ◽  
Power Density ◽  
Coastal Waters ◽  
Wind Field ◽  
Kriging Model ◽  
Offshore Wind ◽  
Offshore Wind Power ◽  
Wind Power Density ◽  
Wind Energy Resources

The predicted wind power in coastal waters is an important factor when planning and developing offshore wind farms. The stochastic wind field challenges the accuracy of these predictions. Using single-point wind measurements, most previous studies have focused on the prediction of short-term wind power, ranging from minutes to several days. Longer-term wind power predictions would better support decision-making related to offshore wind power balance management and reserve capacities. In addition, larger-scale wind power predictions, based on gridded wind field data, would provide a more comprehensive understanding of the spatiotemporal variations of wind energy resources. In this study, a spatiotemporal ordinary kriging model was developed to predict the offshore wind power density on a monthly basis using the cross-calibrated multiplatform gridded wind field data. The spatiotemporal variations of wind power density were directly quantified through the development of spatiotemporal variograms that integrated spatial and temporal distances. The proposed model achieved a notable performance with an overall R2 of 0.94 and a relative prediction error of 16.35% in the validation experiment of predicting the monthly wind power density from 2013 in the coastal waters of China’s Guangdong Province. Using this model, the spatial distributions of wind power density along Guangdong’s coastal waters at monthly, seasonal, and annual time-scales from 2013 were accurately predicted. The experiment results demonstrated the remarkable potential of the spatiotemporal ordinary kriging model to provide reliable long-term prediction for offshore wind energy resources.

scholarly journals Evaluation of the Technical Wind Energy Potential of Kisii Region Based on the Weibull and Rayleigh Distribution Models

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
N. Laban Ongaki ◽  
Christopher M. Maghanga ◽  
Joash Kerongo
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Series Data ◽  
Energy Potential ◽  
Distribution Models ◽  
Wind Power Density ◽  
Wind Speeds

Background. Global warming is a growing threat in the world today mainly due to the emission of CO2 caused by the burning of fossil fuel. Consequently, countries are being forced to seek potential alternative sources of energy such as wind, solar, and photovoltaic among many others. However, the realization of their benefits is faced with challenges. Though wind stands a chance to solve this problem, the lack of adequate site profiles, long-term behavioural information, and specific data information that enables informed choice on site selection, turbine selection, and expected power output has remained a challenge to its exploitation. In this research, Weibull and Rayleigh models are adopted. Wind speeds were analyzed and characterized in the short term and then simulated for a long-term measured hourly series data of daily wind speeds at a height of 10 m. The analysis included daily wind data which was grouped into discrete data and then calculated to represent the mean wind speed, diurnal variations, daily variations, and monthly variations. To verify the models, statistical tools of Chi square, RMSE, MBE, and correlational coefficient were applied. Also, the method of measure, correlate, and predict was adopted to check for the reliability of the data used. The wind speed frequency distribution at the height of 10 m was found to be 2.9 ms-1 with a standard deviation of 1.5. From the six months’ experiments, averages of wind speeds at hub heights of 10 m were calculated and found to be 1.7 m/s, 2.4 m/s, and 1.3 m/s, for Ikobe, Kisii University, and Nyamecheo stations, respectively. The wind power density of the region was found to be 29 W/m2. By a narrow margin, Rayleigh proves to be a better method over Weibull in predicting wind power density in the region. Wind speeds at the site are noted to be decreasing over the years. The region is shown as marginal on extrapolation to 30 m for wind energy generation hence adequate for nongrid connected electrical and mechanical applications. The strong correlation between the site wind profiles proves data reliability. The gradual decrease of wind power over the years calls for attention.

scholarly journals Analysis and Distribution of Wind Speed in Saudi Arabia

2021 ◽  
Author(s):  
Y. Al-Douri ◽  
S. A. Waheeb
Keyword(s):  
Saudi Arabia ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Frequency Distribution ◽  
North West ◽  
Wind Power Density ◽  
Wind Speeds

Abstract Background: the purpose of this study is to estimate the winds, its erosion and wind power of Kingdom of Saudi Arabia. The additional value is to details wind energy in Saudi Arabia indulging updated wind speed analysis, wind speed frequency distribution and mean wind power density variation to present novel work could be added to the literature providing recent data helping for future researches and studies. Results: the updated analysis and distribution of wind energy are presented in six sites; Al Jouf, Hafar Al Batin, Riyadh, Al Wajh, Jeddah South and Sharurah of Saudi Arabia. The winds and wind energy are elaborated. The long-term annual mean values of wind speeds are found to vary between year 2000 and 2020. The annual values of wind power density are varied between year 2006 and 2020. Also, the wind speeds are researched over the entire geography of Saudi Arabia. The percent frequency distribution at different wind speeds for the mentioned six sites at 12 m for two decades is displayed. Conclusions: the long-term values of wind speed were found between 3.3 m/s in 2000 and 5 m/s in 2020. The annual wind power density values were varied between 44 W/m2 in 2006 and 88 W/m2 in 2020. In addition, the wind speeds were researched over the entire Saudi Arabia for east, north, west and south. The deduced percent frequency distribution was less than 18% of the time at 12 m.

scholarly journals Long-term changes in offshore wind power density and wind turbine capacity factor in the Iberian Peninsula (1900–2010)

Energy ◽  
2021 ◽  
Vol 226 ◽  
pp. 120364
Author(s):  
Sheila Carreno-Madinabeitia ◽  
Gabriel Ibarra-Berastegi ◽  
Jon Sáenz ◽  
Alain Ulazia
Keyword(s):  
Wind Turbine ◽  
Iberian Peninsula ◽  
Wind Power ◽  
Power Density ◽  
Capacity Factor ◽  
Offshore Wind ◽  
Offshore Wind Power ◽  
Long Term Changes ◽  
Turbine Capacity

Correction of surface characteristics to diagnostic wind modeling and its impact on potentials of wind power density

2014 ◽  
Vol 6 (4) ◽  
pp. 042012 ◽  
Author(s):  
Dong Hyeok Kim ◽  
Hwa Woon Lee ◽  
Soon Yeong Park ◽  
Jung Woo Yu ◽  
Changhyoun Park ◽  
...  
Keyword(s):  
Wind Power ◽  
Power Density ◽  
Surface Characteristics ◽  
Wind Power Density ◽  
Wind Modeling

scholarly journals Combining QuickSCAT wind data and Landsat ETM+ images to evaluate the offshore wind power resource of East Vietnam Sea

2020 ◽  
Vol 20 (2) ◽  
pp. 143-153
Author(s):  
Nguyen Xuan Tung ◽  
Do Huy Cuong ◽  
Bui Thi Bao Anh ◽  
Nguyen Thi Nhan ◽  
Tran Quang Son
Keyword(s):  
Power Generation ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Geographical Location ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Wind Data ◽  
Power Capacity ◽  
Wind Power Density

Since the East Vietnam Sea has an advantageous geographical location and rich natural resources, we can develop and manage islands and reefs in this region reasonably to declare national sovereignty. Based on 1096 scenes of QuikSCAT wind data of 2006–2009, wind power density at 10 m hight is calculated to evaluate wind energy resources of the East Vietnam Sea. With a combination of wind power density at 70 m hight calculated according to the power law of wind energy profile and reef flats extracted from 35 scenes of Landsat ETM+ images, installed wind power capacity of every island or reef is estimated to evaluate wind power generation of the East Vietnam Sea. We found that the wind power density ranges from levels 4–7, so that the wind energy can be well applied to wind power generation. The wind power density takes on a gradually increasing trend in seasons. Specifically, the wind power density is lower in spring and summer, whereas it is higher in autumn and winter. Among islands and reefs in the East Vietnam Sea, the installed wind power capacity of Hoang Sa archipelago is highest in general, the installed wind power capacity of Truong Sa archipelago is at the third level. The installed wind power capacity of Discovery Reef, Bombay Reef, Tree island, Lincoln island, Woody Island of Hoang Sa archipelago and Mariveles Reef, Ladd Reef, Petley Reef, Cornwallis South Reef of Truong Sa archipelago is relatively high, and wind power generation should be developed on these islands first.

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