Long Term Forecasting of Wind Speed for Wind Energy Application

2018 ◽  
Author(s):  
Nihad Aghbalou ◽  
Abderafi Charki ◽  
Saida R. Elazzouzi ◽  
Kamal Reklaoui
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Energy Application ◽  
Long Term Forecasting

scholarly journals Assessment of wind energy potential in East Azarbaijan province of Iran (Case Stady: Sahand station)

2015 ◽  
Vol 17 (2) ◽  
pp. 418-425
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Fossil Fuels ◽  
Ground Level ◽  
Energy Potential ◽  
Wind Speeds ◽  
Mean Wind Speed ◽  
Wind Energy Potential ◽  
Seasonal Wind

<p>Today&#39;s world requires a change in how the use of different types of energy. With declining reserves of fossil fuels for renewable energies is of course the best alternative. Among the renewable energy from the wind can be considered one of the best forms of energy can be introduced. Accordingly, most countries are trying to identify areas with potential to benefit from this resource.</p> <p>The aim of this study was to assess the potential wind power in Sahand station of Iran country. Hourly measured long term wind speed data of Sahand during the period of 2000-2013 have been statistically analyzed. In this study the wind speed frequency distribution of location was found by using Weibull distribution function. The wind energy potential of the location has been studied based on the Weibull mode. The results of this study show that mean wind speed measured at 10 m above ground level is determined as 5.16 m/s for the studied period. This speed increases by, respectively, 34.78 % and 41.21 %, when it is extrapolated to 40 and 60 m hub height.</p> <div> <p>Long term seasonal wind speeds were found to be relatively higher during the period from January to September. At the other hand, higher wind speeds were observed between the period between 06:00 and 18:00 in the day. These periods feet well with annual and daily periods of maximum demand of electricity, respectively.&nbsp;</p> </div> <p>&nbsp;</p>

Characterizing Wind Speed and Air Density for Wind Energy Estimation

2009 ◽  
Author(s):  
Xiuli Qu ◽  
Jing Shi
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Density Distribution ◽  
North Dakota ◽  
Distribution Model ◽  
Energy Potential ◽  
Energy Estimation ◽  
Air Density ◽  
Wind Energy Potential

Wind energy is the fastest growing renewable energy source in the past decade. To estimate the wind energy potential for a specific site, the long-term wind data need to be analyzed and accurately modeled. Wind speed and air density are the two key parameters for wind energy potential calculation, and their characteristics determine the long-term wind energy estimation. In this paper, we analyze the wind speed and air density data obtained from two observation sites in North Dakota and Colorado, and the variations of wind speed and air density in long term are demonstrated. We obtain univariate statistical distributions for the two parameters respectively. Excellent fitting performance can be achieved for wind speed for both sites using conventional univariate probability distribution functions, but fitting air density distribution for the North Dakota site appears to be less accurate. Furthermore, we adopt Farlie-Gumbel-Morgenstern approach to construct joint bivariate distributions to describe wind speed and air density simultaneously. Overall, satisfactory goodness-of-fit values are achieved with the joint distribution models, but the fitting performance is slightly worse compared with the univariate distributions. Further research is needed to improve air density distribution model and the joint bivariate distribution model for wind speed and air density.

scholarly journals The Forecasting Procedure for Long-Term Wind Speed in the Zhangye Area

2010 ◽  
Vol 2010 ◽  
pp. 1-17 ◽  
Author(s):  
Zhenhai Guo ◽  
Yao Dong ◽  
Jianzhou Wang ◽  
Haiyan Lu
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Alternative Energy ◽  
Energy System ◽  
Support Vector ◽  
Ancillary Service ◽  
Average Wind Speed ◽  
Index Method ◽  
Wind Speed Forecasting

Energy crisis has made it urgent to find alternative energy sources for sustainable energy supply; wind energy is one of the attractive alternatives. Within a wind energy system, the wind speed is one key parameter; accurately forecasting of wind speed can minimize the scheduling errors and in turn increase the reliability of the electric power grid and reduce the power market ancillary service costs. This paper proposes a new hybrid model for long-term wind speed forecasting based on the first definite season index method and the Autoregressive Moving Average (ARMA) models or the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) forecasting models. The forecasting errors are analyzed and compared with the ones obtained from the ARMA, GARCH model, and Support Vector Machine (SVM); the simulation process and results show that the developed method is simple and quite efficient for daily average wind speed forecasting of Hexi Corridor in China.

scholarly journals Wind Potential Assessment Analysis of Jhampir, District Thatta Sindh, Pakistan

2019 ◽  
Vol 38 (3) ◽  
pp. 571-580
Author(s):  
Aftab Ahmad ◽  
Fareed Husssain Mangi ◽  
Yasir Fazlani ◽  
Athar Chachar ◽  
Kashif Khan
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Potential Analysis ◽  
Nearby Region ◽  
Sindh Province ◽  
Wind Potential ◽  
Class 1 ◽  
Mean Wind Speed ◽  
Correct Estimation

Wind potential analysis is analyzing how much wind energy is available in particular region. It is most important step because the economics of project depends on the site wind resources. Wind plant depends on the variation of long-term mean wind speed and other characteristics which vary from a distance to distance. This study discusses the wind speed characteristics and wind potential analysis using three years 2014-2016 wind data of Jhampir located in district Thatta, situated in the Southeast of Sindh province. The numerical Weibull distribution approach is used to estimate parameters. The correct estimation of wind parameters and class is essential before developing any wind project in the region. The data used in this study is measured at 80 m height. The region is classified as from class 1-7. The results show that monthly mean speed values lie between 4.79-10.96 m/s. The annual mean scale and shape parameters lie in the range of 7.42-7.59. The wind power density was found in a range of 303.31355.64. This study is related to the decision-making process on a significant wind project in Thatta or nearby region. The stable wind energy pattern is observed in the region for harnessing wind energy almost throughout the year. The Weibull probability density curves also indicate a trend of a boost in the chances of observing wind from 2014-2016.

Long-term climate change effects in the European offshore wind energy 

2021 ◽  
Author(s):  
Stefano Susini ◽  
Melisa Menendez
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Climate Change Impact ◽  
Energy Production ◽  
Offshore Wind ◽  
Change Impact

&lt;p&gt;Climate change and offshore renewable energy sector are connected by a double nature link. Even though energy generation from clean marine sources is one of the strategies to reduce climate change impact within next decades, it is expected that large scale modification of circulation patterns will have in turn an impact on the spatial and temporal distribution of the wind fields. Under the WINDSURFER project of the ERA4CS initiative, we analyse the climate change impact on marine wind energy resource for the European offshore wind energy sector. Long-term changes in specific climate indicators are evaluated over the European marine domain (e.g. wind power density, extreme winds, operation hours) as well as local indicators (e.g. gross energy yield, capacity factor) at several relevant operating offshore wind farms.&lt;/p&gt;&lt;p&gt;Adopting an ensemble approach, we focus on the climate change greenhouse gases scenario RCP8.5 during the end of the century (2081-2100 period) and analyze the changes and uncertainty of the resulting multi-model from seven high resolution Regional Climate Models (RCM) realized within Euro-Cordex initiative (EUR-11, ~12.5km). ERA5 reanalysis and in-situ offshore measurements are the historical data used in present climate.&lt;/p&gt;&lt;p&gt;Results indicate a small decrease of wind energy production, testified by reduction of the climatological indicators of wind speed and wind power density, particularly in the NW part of the domain of study. The totality of the currently operating offshore windfarms is located in this area, where a decrease up to 20% in the annual energy production is expected by the end of the century, accompanied by a reduction of the operation hours between 5 and 8%. Exceptions are represented by Aegean and Baltic Sea, where these indicators are expected to slightly increase. Extreme storm winds however show a different spatial pattern of change. The wind speed associated to 50 years return period decreases within western Mediterranean Sea and Biscay Bay, while increases in the remaining part of the domain (up to 15% within Aegean and Black Sea). Finally, the estimated variations in wind direction are relevant on the Biscay Bay region.&lt;/p&gt;

Telescopic Wind Turbines to Capture Energy at Low Wind Speeds

2009 ◽  
Author(s):  
Mustahib Imraan ◽  
Rajnish N. Sharma ◽  
Richard G. J. Flay
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Energy Conversion ◽  
Wind Turbine ◽  
Power Output ◽  
Wind Energy Conversion System ◽  
Wind Energy Conversion ◽  
Conversion System ◽  
Energy Conversion System

The reduction in cost of energy of wind turbines requires many technical contributions from all areas of the Wind Energy Conversion System. The variations in the wind (e.g. Diurnal, Monthly, Seasonal and Long term) as normally shown on probability density distributions directly affect the wind turbine performance. The turbine power output is also dependent upon a number of other variables, and a lot of research has been carried out to increase the power coefficient that has an upper limit of 0.593 called the Betz Limit. A possible way for improving the power output of a turbine is to control the swept area by controlling the diameter of the rotor. Ideally the wind turbine designer will use the long term mean wind speed to design and establish the rated power output of the wind energy conversion system. The stochastic nature of wind will fluctuate the power output of the turbine. Therefore to maintain the design rated power of the turbine, the telescopic wind turbine concept can be used. When the wind speed drops, the telescopic blades extend in order to maintain the power output, and when the wind speed increases, the telescopic blades retract in order to reduce the loads on the system. By telescoping the blades, the capacity factor of the wind energy conversion system is thus enhanced. The wind energy characteristic of a region in New Zealand was studied and the results show an 18% increase in annual energy production of a 10 kW wind turbine with telescopic blades.

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