Comparison between the bivariate Weibull probability approach and linear regression for assessment of the long-term wind energy resource using MCP

A detailed investigation of a measure–correlate–predict (MCP) approach based on the bivariate Weibull (BW) probability distribution of wind speeds at pairs of correlated sites has been conducted. Since wind speeds are typically assumed to follow Weibull distributions, this approach has a stronger theoretical basis than widely used regression MCP techniques. Building on previous work that applied the technique to artificially generated wind data, we have used long-term (11 year) wind observations at 22 pairs of correlated UK sites. Additionally, 22 artificial wind data sets were generated from ideal BW distributions modelled on the observed data at the 22 site pairs. Comparison of the fitting efficiency revealed that significantly longer data periods were required to accurately extract the BW distribution parameters from the observed data, compared to artificial wind data, due to seasonal variations. The overall performance of the BW approach was compared to standard regression MCP techniques for the prediction of the 10 year wind resource using both observed and artificially generated wind data at the 22 site pairs for multiple short-term measurement periods of 1–12 months. Prediction errors were quantified by comparing the predicted and observed values of mean wind speed, mean wind power density, Weibull shape factor and standard deviation of wind speeds at each site. Using the artificial wind data, the BW approach outperformed the regression approaches for all measurement periods. When applied to the real wind speed observations however, the performance of the BW approach was comparable to the regression approaches when using a full 12 month measurement period and generally worse than the regression approaches for shorter data periods. This suggests that real wind observations at correlated sites may differ from ideal BW distributions and hence regression approaches, which require less fitting parameters, may be more appropriate, particularly when using short measurement periods.     

A new probabilistic method to estimate the long-term wind speed characteristics at a potential wind energy conversion site

This paper proposes the use of a new Measure-Correlate-Predict (MCP) method to estimate the long-term wind speed characteristics at a potential wind energy conversion site. The proposed method uses the probability density function of the wind speed at a candidate site conditioned to the wind speed at a reference site. Contingency-type bivariate distributions with specified marginal distributions are used for this purpose. The proposed model was applied in this paper to wind speeds recorded at six weather stations located in the Canary Islands (Spain). The conclusion reached is that the method presented in this paper, in the majority of cases, provides better results than those obtained with other MCP methods used for purposes of comparison. The metrics employed in the analysis were the coefficient of determination (R²) and the root relative squared error (RRSE). The characteristics that were analysed were the capacity of the model to estimate the long-term wind speed probability distribution function, the long-term wind power density probability distribution function and the long-term wind turbine power output probability distribution function at the candidate site.     

An analysis of wind power density derived from several wind speed density functions: The regional assessment on wind power in Malaysia

In wind turbine design and site planning, the probability distribution of wind speed becomes critically important in estimating energy production. The utilization of accurate distribution will minimize the uncertainty in wind resource estimates, and consequently, it will improve the result in the site assessment phase of planning. In general, different region will have different wind regime. Hence, it is reasonable that different wind speed distribution will be found for different region. In this study, the features of wind power density based on the dependency of the suitable wind speed density have been obtained analytically using transformation technique. Since the wind power density has been obtained, the mean power density which is referred as an important indices related to the estimation of potential wind energy have been obtained by using the concept of raw moment and Monte Carlo approach. An analysis of semivariogram indicates the lack of spatial correlation of the wind power in Malaysia. The map of the mean power density over Malaysia indicates that several regions such as northeast, northwest and southeast region of Peninsular Malaysia and southern region of Sabah are found as the best region to be further investigated in the future for the wind energy development.     

Potential wind power generation in the State of Kuwait

The wind characteristics of six locations in the State of Kuwait have been assessed. The annual average wind speed for the considered sites ranged from 3.7 to 5.5 m/s and a mean wind power density from 80 to 167 W/m² at standard height of 10 m. The Weibull parameters and power density of each station have been determined using Weibull distribution. The wind data at heights 15, 20, 25 and 30 m were obtained by extrapolation of the 10 m data using the Power-Law. The potential wind energy at different heights was estimated using Weibull parameters. Maximum power density is found at 30 m height which varies between 130 and 275 W/m² with 70% increase from the standard height indicating fairly potential wind energy especially in the northern part of the country. The highest potential wind power was found during the summer season which is the peak demand season of electricity in Kuwait.     

On wind speed pattern and energy potential in Nigeria

The aim of this paper is to review wind speed distribution and wind energy availability in Nigeria and discuss the potential of using this resource for generation of wind power in the country. The power output from a wind turbine is strongly dependent on the wind speed and accurate information about the wind data in a targeted location is essential. The annual mean wind speeds in Nigeria range from about 2 to 9.5 m/s and the annual power density range between 3.40 and 520 kW/m² based on recent reported data. The trend shows that wind speeds are low in the south and gradually increases to relatively high speeds in the north. The areas that are suitable for exploitation of wind energy for electricity generation as well as for water pumping were identified. Also some of the challenges facing the development of wind energy and suggested solutions were presented.     

Assessment of wind characteristics and wind turbine characteristics in Taiwan

Wind characteristics and wind turbine characteristics in Taiwan have been thoughtfully analyzed based on a long-term measured data source (1961–1999) of hourly mean wind speed at 25 meteorological stations across Taiwan. A two-stage procedure for estimating wind resource is proposed. The yearly wind speed distribution and wind power density for the entire Taiwan is firstly evaluated to provide annually spatial mean information of wind energy potential. A mathematical formulation using a two-parameter Weibull wind speed distribution is further established to estimate the wind energy generated by an ideal turbine and the monthly actual wind energy generated by a wind turbine operated at cubic relation of power between cut-in and rated wind speed and constant power between rated and cut-out wind speed. Three types of wind turbine characteristics (the availability factor, the capacity factor and the wind turbine efficiency) are emphasized. The monthly wind characteristics and monthly wind turbine characteristics for four meteorological stations with high winds are investigated and compared with each other as well. The results show the general availability of wind energy potential across Taiwan.     

Wind characteristics and energy potentials for Owerri, Nigeria

Wind characteristics and wind energy resource potentials for Owerri, Nigeria are presented. These were evaluated using routine wind data measurements at a height of 10 m above ground level at the Lake Nwebere Campus, Federal University of Technology, Owerri between 1988 and 1992. The most prevailing wind is from the Southwest and the average wind speed and its variation are 2.80 and 0.81 m s⁻¹, respectively.Accordingly, the maximum annual mean power density exploitable from the wind at this site is 7.66 ± 0.15 W m⁻² out of the estimated available annual mean wind power density of 12.91 ± 0.26 W m⁻². The annual mean energy density available in the wind was found to be 60.29 kW h m⁻². Thus, the potential for year-round wind energy utilization in Owerri, Nigeria is rather low.     

An investigation on wind power potential of Nurda ı-Gaziantep, Turkey

Turkey is one of the developing countries. The production of electricity in Turkey is basically focused on hydro-power and thermal-power. On the other hand, measurements show that Turkey has a reasonable wind potential but this potential was not being used for many years due to government policies which supported the use of petroleum, coal, and hydro power as energy sources. In recent years there is an increasing interest in using wind energy as one of the energy sources. This paper briefly introduces a study of the determination of wind power potential of Nurda ı/Gaziantep district where is on the south of Turkey for future wind power generation projects. Evaluation of wind data; taken by Turkish Electrical Power Resources Development Administration at the foot of the mountain, Nurda ı, shows that the district has a mean wind speed of 7.3 m/s at 10 m height and observed highest value wind speed is 23.3 m/s. Mean power density of the site is found as 222 W/m² and the results suggest that the site encourages investors especially since the terrain is a grassy plain on the side of the mountain and the measurements are taken at 10 m height.     

Investigation of wind power density at different heights in the Gelibolu peninsula of Turkey

This study aims to determine the wind characteristics and wind power potential of the Gelibolu peninsula in the Çanakkale region of Turkey. For this purpose, hourly average wind data observed at the Gelibolu meteorological station were used. The Weibull probability density functions and Weibull parameters of time-series of wind speed, mean wind speed, and mean wind power potential were determined for different heights as 10, 20, 30, 40, and 50 m. According to the results obtained at 10- and 50-m heights above the ground level, the annual wind speed varied from 6.85 to 8.58 m/s in this region, respectively. The annual wind power potential of the site was determined as 407 and 800 W/m² for 10- and 50-m heights, respectively. These results indicate that the investigated site has a reasonable wind power potential for generating electricity.     

Examining wind quality and wind power prospects on Fiji Islands

Wind resource analysis was carried out for two major islands in the Fiji. Wind data from July 1993 to June 2005 from NASA data base was analysed. Annual seasonal variation in wind speed, direction and power density were analysed for various locations. The average yearly wind speed for Fiji is between 5 and 6 m/s with average power density of 160 W/m². Site specific validation showed no significant relationship between NASA and experimental data. The wind resource at Laucala Bay has a power density of 131 W/m² at 55 m. The expected annual energy produced from a 275 kW GEV Vergnet wind turbine is 344 MWh. The capacity factor of the turbine is expected to be 14.3% with an overall efficiency of 37%. The electricity generated would cost $FJ 0.27 per kWh. The system will payback its worth in 12.2 years.     

Potential for wind generation on the Guyana coastlands

Guyanas dependence upon imported petroleum fuels can only be offset by the sustained exploitation of its indigenous resources. With its populated coastlands exposed to the northeast trade winds and a history of small-scale wind energy utilisation wind is one such potential energy source. In this study, the coastal wind regime is analysed and historical data from a coastal weather station are used to estimate the potential for wind generation. It is found that a hybrid Weibull probability density function best describes the annual wind speed frequency distribution at the reference height of 10.67 m. With an annual mean wind speed of 5.8 ms, an energy pattern factor of 1.41, and an annual average power density of 159 Wm², this distribution represents a class-3 wind resource, suitable for most wind turbine applications. Site analysis and observed trends in coastal wind availability suggest the strong likelihood of a greater wind resource in more open locations. In view of its apparent potential for wind farm operation, a comprehensive, wind resource assessment programme is recommended for the Guyana coastlands.     

An onsite demonstration and validation of LiDAR technology for wind energy resource assessment

The investigation of wind resource at higher heights is very crucial in planning wind power project. Normally, this involves the installation of a high and costly meteorological mast with a cup anemometer and wind vanes. This investigation uses the new ground-based remote-sensing technique Light Detection and Ranging (LiDAR) to investigate the wind resource at higher heights. This paper describes the LiDAR technology principle and examines the potential of LiDAR measurement to estimate the wind resource at higher heights by conducting a measurement campaign at Tamil Nadu, India. The wind statistics were determined using the 10?min average time-series wind data monitored by ZephIR LiDAR. These include the Weibull parameters, daily mean wind speed, wind power density, wind energy density, vertical wind speed profile and capacity factor. The investigation reveals that the vertical wind speed profile measured from the LiDAR system has approximate closer values to the standard meteorological measurement.     

Using nacelle‐based wind speed observations to improve power curve modeling for wind power forecasting

Wind power forecasting for projection times of 0–48 h can have a particular value in facilitating the integration of wind power into power systems. Accurate observations of the wind speed received by wind turbines are important inputs for some of the most useful methods for making such forecasts. In particular, they are used to derive power curves relating wind speeds to wind power production. By using power curve modeling, this paper compares two types of wind speed observations typically available at wind farms: the wind speed and wind direction measurements at the nacelles of the wind turbines and those at one or more on‐site meteorological masts (met masts). For the three Australian wind farms studied in this project, the results favor the nacelle‐based observations despite the inherent interference from the nacelle and the blades and despite calibration corrections to the met mast observations. This trend was found to be stronger for wind farm sites with more complex terrain. In addition, a numerical weather prediction (NWP) system was used to show that, for the wind farms studied, smaller single time‐series forecast errors can be achieved with the average wind speed from the nacelle‐based observations. This suggests that the nacelle‐average observations are more representative of the wind behavior predicted by an NWP system than the met mast observations. Also, when using an NWP system to predict wind farm power production, it suggests the use of a wind farm power curve based on nacelle‐average observations instead of met mast observations. Further, it suggests that historical and real‐time nacelle‐average observations should be calculated for large wind farms and used in wind power forecasting. Copyright © 2011 John Wiley & Sons, Ltd.     

Investigation of wind characteristics and assessment of wind energy potential for Waterloo region,Canada

Wind energy becomes more and more attractive as one of the clean renewable energy resources. Knowledge of the wind characteristics is of great importance in the exploitation of wind energy resources for a site. It is essential in designing or selecting a wind energy conversion system for any application. This study examines the wind characteristics for the Waterloo region in Canada based on a data source measured at an elevation 10 m above the ground level over a 5-year period (1999–2003) with the emphasis on the suitability for wind energy technology applications. Characteristics such as annual, seasonal, monthly and diurnal wind speed variations and wind direction variations are examined. Wind speed data reveal that the windy months in Waterloo are from November to April, defined as the Cold Season in this study, with February being the windiest month. It is helpful that the high heating demand in the Cold Season coincides with the windy season. Analysis shows that the day time is the windy time, with 2 p.m. in the afternoon being the windiest moment. Moreover, a model derived from the maximum entropy principle (MEP) is applied to determine the diurnal, monthly, seasonal and yearly wind speed frequency distributions, and the corresponding Lagrangian parameters are determined. Based on these wind speed distributions, this study quantifies the available wind energy potential to provide practical information for the application of wind energy in this area. The yearly average wind power density is 105 W/m². The day and night time wind power density in the Cold Season is 180 and 111 W/m², respectively.     

Viability and economic analysis of wind energy resource for power generation in Johannesburg,South Africa

In this study, wind characteristics and wind power potential of Johannesburg are investigated using 5-min average time series wind speed collected between 2005 and 2009 at anemometer height of 10 m. The statistical distribution that best fits the empirical wind speed data at the site of study is first determined based on the coefficient of determination and root mean square error criteria. The statistical parameters and wind power density based on this model are estimated for different months of the year using standard deviation method. Economic analyses of some wind turbines are also carried out. Some of the key results show that the site is only suitable for small wind turbines in a standalone application. A 10 kW wind turbine with cut-in wind speed of 3.5 m/s, rated wind speed of 9 m/s, and cut-out wind speed of 25 m/s seems most appropriate in Johannesburg with the lowest cost that varies from 0.25 to 0.33 $/kWh.     

Simulation of hourly wind speed and array wind power

Statistical summaries of wind speed are sufficient to compute many characteristics of turbine-generated power, such as the mean, variance and reliability of various power levels. However, a wind speed time series is necessary to produce a sequence of power values as used for investigating load matching and storage requirements. Since a long historical record of wind speed may not be available at a wind turbine candidate site, it is desirable to be able to generate a simulated numerical sequence of hourly wind speed values. Two such approximate procedures are developed in this paper. One procedure generates sequential wind speed values at a site based on the Weibull parameters of hourly wind speed and the lag-one autocorrelation of hourly wind speed values. Comparison with historical data at a site is made. The second procedure generates sequential hourly wind power values for a regional array of wind turbines. It utilizes the typical site wind characteristics, the spatial and lag-one cross correlation and autocorrelation of hourly wind speed values and an equivalent linearized relationship between array average wind speed and array power. Comparison with results for six different wind turbines in three different regional arrays indicates good agreement for wind power histograms, autocorrelation function and mean persistence.     

Comparison between ANNs and linear MCP algorithms in the long-term estimation of the cost per kW h produced by a wind turbine at a candidate site: A case study in the Canary Islands

In the work presented in this paper Artificial Neural Networks (ANNs) were used to estimate the long-term wind speeds at a candidate site. The specific costs of the wind energy were subsequently determined on the basis of the knowledge of these wind speeds. The results were compared with those obtained with a linear Measure–Correlate–Predict (MCP) method. The mean hourly wind speeds and directions recorded over a 10 year period at six weather stations located on different islands in the Canary Archipelago (Spain) were used as a case study. The power-wind speed curves for five wind turbines of different rated power were also used. The mean absolute percentage error (MAPE), Pearson’s correlation coefficient and the Index of Agreement (IoA) between measured and estimated data were used to evaluate the errors made with the different metrics analysed.     

Assessment of wind energy in Iran: A review

In this study, a ten minute period measuring wind speed data for year 2007 at 10 m, 30 m and 40 m heights for different places in Iran, has been statistically analyzed to determine the potential of wind power generation. Sixty eight sites have been studied. The objective is to evaluate the most important characteristics of wind energy in the studied sites. The statistical attitudes permit us to estimate the mean wind speed, the wind speed distribution function, the mean wind power density and the wind rose in the site at three different heights. Some local phenomena are also considered in the characterization of the site.     

Evaluating the performance of wind turbines in selected locations in Oyo state,Nigeria

The wind speed distribution and wind energy potential are investigated in three selected locations in Oyo state using wind speed data that span between 12 and 20 years measured at 10 m height. In addition, the performance of selected small to medium size wind turbines in these sites were examined. The annual energy output and capacity factor for these turbines were determined. It was found that the monthly mean wind speeds in Oyo state ranges from 2.85 m/s to 5.20 m/s. While the monthly mean power density varies between 27.08 W/m² and 164.48 W/m², while the annual mean power density is in the range of 67.28 W/m² and 106.60 W/m². Based on annual energy output, wind turbines with cut-in wind speed of about 2.5 m/s and moderate rated wind speeds will be best suited for all the sites.     

Effectiveness of WRF wind direction for retrieving coastal sea surface wind from synthetic aperture radar

Wind direction is required as input to the geophysical model function (GMF) for the retrieval of sea surface wind speed from a synthetic aperture radar (SAR) images. The present study verifies the effectiveness of using the wind direction obtained from the weather research and forecasting model (WMF) as input to the GMF to retrieve accurate wind fields in coastal waters adjacent to complex onshore terrain. The wind speeds retrieved from 42 ENVISAT ASAR images are validated based on in situ measurements at an offshore platform in Japan. Accuracies are also compared with cases using wind directions: the meso‐analysis of the Japan Meteorological Agency (MANAL), the SeaWinds microwave scatterometer on QuikSCAT and the National Center for Environmental Prediction final operational global analysis data (NCEP FNL). In comparison with the errors of the SAR‐retrieved wind speeds obtained using the WRF, MANAL, QuikSCAT and NCEP FNL wind directions, the magnitudes of the errors do not appear to be correlated with the errors of the wind directions themselves. In addition to wind direction, terrain factors are considered to be a main source of error other than wind direction. Focusing on onshore winds (blowing from the sea to land), the root mean square errors on wind speed are found to be 0.75 m s ^{? 1} (in situ), 0.96 m s ^{? 1} (WRF), 1.75 m s ^{? 1} (MANAL), 1.58 m s ^{? 1} (QuikSCAT) and 2.00 m s ^{? 1} (NCEP FNL), respectively, but the uncertainty is of the same order of magnitude because of the low number of cases. These results indicate that although the effectiveness of using the accurate WRF wind direction for the wind retrieval is partly confirmed, further efforts to remove the error due to factors other than wind direction are necessary for more accurate wind retrieval in coastal waters. Copyright © 2012 John Wiley & Sons, Ltd.