Spatial estimation of wind speed

This study utilizes Abductory Induction Mechanism to estimate the mean monthly wind speed at some locations in Saudi Arabia based on wind data at other available recording stations in addition to some historical wind speed data at the target site. Wind speed data from 20 meteorological stations over a period of 16 years between 1990 and 2005 was used to accomplish the set objective. To validate the model, data from 19 stations was used to estimate the wind speed at the 20th location. Evaluation was performed for every one of the 20 available locations. Results show good agreement between estimated and measured monthly mean wind speed values. Copyright © 2010 John Wiley & Sons, Ltd.     

The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria

Modelling and prediction of wind speed are essential prerequisites in the sitting and sizing of wind power applications. The profile of wind speed in Nigeria is modelled using artificial neural network (ANN). The ANN model consists of 3-layered, feed-forward, back-propagation network with different configurations, designed using the Neural Toolbox for MATLAB. The monthly mean daily wind speed data monitored at 10 m above ground level for a period of 20 years (1983–2003) for 28 ground stations operated by the Nigeria Meteorological Services (NIMET) were used as training (18 stations) and testing (10 stations) dataset. The geographical parameters (latitude, longitude and altitude) and the month of the year were used as input data, while the monthly mean wind speed was used as the output of the network. The optimum network architecture with minimum Mean Absolute Percentage Error (MAPE) of 8.9% and correlation coefficient (r) between the predicted and the measured wind speed values of 0.9380 was obtained. The predicted monthly wind speed ranged from 0.9–13.1 m/s with an annual mean of 4.7 m/s. The model predicted wind speed values are given in the form of monthly maps, which can be easily used for assessment of wind energy potential for different locations within Nigeria.     

The potential of electricity generation on the east coast of Red Sea in Egypt

Wind characteristics have been analyzed based on long-term measured data of monthly mean wind speed of seven meteorological stations along the east coast of Red Sea in Egypt. It was found that the windiest stations (Region A) namely (Zafarana, Abu Darag, Hurghada and Ras Benas) have annual mean wind speeds (7.3, 7.2, 6.4 and 5.5 m/s) at 10 m height, respectively.Numerical estimations using measured wind speeds and frequencies to calculate the two Weibull parameters were carried out and two methods were applied.The methodical analysis for the corrected monthly wind power density at a height of 10 m above ground level, over roughness class 0 (water), for each station was done. The recommended correlation equation was also stated for Red Sea zone in Egypt. Also the corrected annual wind power density at the heights (50–70) m was obtained for all stations. Moreover, calculations show that the four stations in (Region A) have a huge energy potential available (430–1000 W/m²) at 70 m height, while Quseir and Suez stations (Region B) have good wind power density (170–190 W/m²) at 50 m height.A technical and economic assessment has been made of electricity generation from two turbines machines having capacity of (1000 and 600 kW) considered in Regions A & B, respectively, using WASP program. The yearly energy output, capacity factor and the electrical energy cost of kWh produced by the two different turbines in each region were estimated. The production costs of four stations in Region A was found to be less than 2€ cent/kWh and compared with retail tariff.     

A classification mechanism for determining average wind speed and power in several regions of Turkey using artificial neural networks

In this paper, average wind speed and wind power values are estimated using artificial neural networks (ANNs) in seven regions of Turkey. To start with, a network has been set up, and trained with the data set obtained from several stations—each station gather data from five different heights—from each region, one randomly selected height value of a station has been used as test data. Wind data readings corresponding to the last 50 years of relevant regions were obtained from the Turkish State Meteorological Service (TSMS). The software has been developed under Matlab 6.0. In the input layer, longitude, latitude, altitude, and height are used, while wind speeds and related power values correspond to output layer. Then we have used the networks to make predictions for varying heights, which are not incorporated to the system at the training stage. The network has successfully predicted the required output values for the test data and the mean error levels for regions differed between 3% and 6%. We believe that using ANNs average wind speed and wind power of a region can be predicted provided with lesser amount of sampling data, that the sampling mechanism is reliable and adequate.     

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.     

Evaluation of wind energy potential and electricity generation on the coast of Mediterranean Sea in Egypt

Wind data from 10 coastal meteorological stations along the Mediterranean Sea in Egypt have been used for statistical analysis to determine the wind characteristics. It was found that three stations show annual mean wind speed greater than 5.0 m/s. In order to identify the Weibull parameters for all stations two different methods were applied.The methodical analysis for all stations was done for the corrected monthly and annual mean wind power at a height of 10 m, over roughness class 0 (water). The recommended correlation equation was also stated for Mediterranean Sea zone in Egypt. Also the wind power densities for heights of 30–50 m were calculated for all stations. Three of them are the best locations, namely: Sidi Barrani, Mersa Matruh, and El Dabaa, where these contiguous stations have great abundantly wind energy density.A technical assessment has been made of the electricity generation using WASP program for two commercial turbines (300 kW and 1 MW) considering at the three promising sites. The wind turbine of capacity 1 MW was found to produce an energy output per year of 2718 MW h at El Dabaa station, and the production costs was found 2€ cent/kW h.     

The wind energy potential of Thailand

Routine wind data from meteorological stations have been used to determine seasonal wind speed distributions and mean power densities at the surface over Thailand. Analyses of hourly wind speeds at two stations show that Weibull distributions fit the data well, provided that observations of calm are excluded. The diurnal variation of the wind at these stations has also been found. Estimates of mean power densities of surface winds over the whole country are typically in the range 10–20 Wm^?2. Upper level climatic charts indicate that mean free-stream wind power densities above the surface boundary layer are typically in the range 100–600 Wm^?2. Similar power densities would be accessible to wind machines on high ground in many places, depending on mountain topography and machine siting.     

Wind energy evaluation for electricity generation using WECS in seven selected locations in Nigeria

This paper statistically examine wind characteristics from seven meteorological stations within the North-West (NW) geo-political region of Nigeria using 36-year (1971–2007) wind speed data measured at 10 m height subjected to 2-parameter Weibull analysis. It is observed that the monthly mean wind speed in this region ranges from 2.64 m/s to 9.83 m/s. The minimum monthly mean wind speed was recorded in Yelwa in the month of November while the maximum value is observed in Katsina in the month of June. The annual wind speeds range from 3.61 m/s in Yelwa to 7.77 m/s in Kano. It is further shown that Sokoto, Katsina and Kano are suitable locations for wind turbine installations with annual mean wind speeds of 7.61, 7.45 and 7.77 m/s, respectively. The results also suggest that Gusau and Zaria should be applicable for wind energy development using taller wind turbine towers due to their respective annual mean speeds and mean power density while Kaduna is considered as marginal. In addition, higher wind speeds were recorded in the morning hours than afternoon periods for this region. A technical electricity generation assessment using four commercial wind turbines were carried out. The results indicate that, while the highest annual power is obtained with Nordex N80–2.5 MW as 14233.53 kW/year in Kano, the lowest is in Yelwa having 618.06 kW/year for Suzlon S52. It is further shown that the highest capacity factor is 64.95% for Suzlon S52–600 kW in Kano while the lowest is 3.82% for Vestas V80–2 MW in Yelwa.     

Some statistical characteristics of monthly average wind speed at various heights

The article, which is a segment of a complex wind energy examination, uses statistical methods to analyze the time series of monthly average wind speed in the period between 1991 and 2000 measured on seven Hungarian meteorological stations. Empirical distribution of measured monthly average wind speeds is approximated by theoretical distributions to claim that certain distributions are universal, i.e. independent of orography. We used one of them, the Weibull distribution, to generate the distribution of monthly average wind speeds on levels different from anemometer altitude as well, then we calculate the averages for the entire period and we fit a power function on them. Thus we can demonstrate a correlation between Hellmann's wind profile law and the Weibull distribution.     

Correlation of wind speed between neighboring measuring stations

A method for establishing wind speed correlation between neighboring measuring stations is presented in this paper. The aim of this study is to develop a model, in which given the wind speed at a particular site to simulate the wind speed at another, nearby site, in order to estimate the wind power of an area. This method takes into account the evolution of the sample cross correlation function (SCCF) of wind speed in time domain and uses an artificial neural network to perform the wind speed simulation. Four separate pairs of wind data measuring stations at two different regions were examined. Tests showed that the higher the SCCF value between two sites, the better simulation achieved. Also, in a pair of stations under investigation the reference station must be the one that contains more information in its wind speed signal, in order to obtain the optimum simulation performance.     

Effect of direction on wind speed estimation in complex terrain using neural networks

A method of estimating the annual average wind speed at a selected site using neural networks is presented. The method proposed uses only a few measurements taken at the selected site in a short time period and data collected at nearby fixed stations.The neural network used in this study is a multilayer perceptron with one hidden layer of 15 neurons, trained by the Bayesian regularization algorithm. The number of inputs that must be used in the neural network was analyzed in detail, and results suggest that only wind speed and direction data for a single station are required. In sites of complex terrain, direction is a very important input that can cause a decrease of 23% in root mean square (RMS).The results obtained by simulating the annual average wind speed at the selected site based on data from nearby stations are satisfactory, with errors below 2%.     

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.     

A survey on the assessment of wind energy potential in Egypt

In this paper, wind data obtained from the Egyptian Meteorological Authority are used to assess monthly and annual wind power and wind energy. The study is based on data from 15 anemometer meteorological stations, distributed all over Egypt and covering a period ranging from 1973 to 1994. For these stations the wind data are summarized. The wind energy potential at the 25 m height was obtained by extrapolation of data at 10 m using a power-law expression. The result presents the mean wind energy density estimates and potential for application in Egypt. The analysis showed that along Red Sea coasts, the annual wind energy flux is found to be high, which indicates that these coastal stations are possible locations for wind energy utilization. On both the Mediterranean coast and in the interior parts of Egypt, some stations are of low available wind energy, while others are found to be rather high. Also, the two Weibull distribution parameters have been estimated from the wind speed data for some meteorological stations and the wind power density is calculated using the values of these parameters.     

Use of artificial neural networks for mapping of solar potential in Turkey

Turkey has sufficient solar radiation intensities and radiation durations for solar thermal applications since Turkey lies in a sunny belt, between 36° and 42° N latitudes. The yearly average solar-radiation is 3.6 kWh/m²day, and the total yearly radiation period is ∼2610 h. The main focus of this study is to determine the solar-energy potential in Turkey using artificial neural-networks (ANNs). Scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM) learning algorithms and a logistic sigmoid transfer function were used in the network. In order to train the neural network, meteorological data for the last 3 years (2000–2002) from 17 stations (namely cities) spread over Turkey were used as training (11 stations) and testing (6 stations) data. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, and mean temperature) are used as inputs to the network. Solar radiation is in the output layer. The maximum mean absolute percentage error was found to be less than 6.7% and R² values to be about 99.8937% for the testing stations. However, the respective values were found to be 2.41 and 99.99658% for the training stations. The trained and tested ANN models show greater accuracies for evaluating solar resource posibilities in regions where a network of monitoring stations has not been established in Turkey. The predicted solar-potential values from the ANN were given in the form of monthly maps. These maps are of prime importance for different working disciplines, like those of scientists, architects, meteorologists, and solar engineers in Turkey. The predictions from ANN models could enable scientists to locate and design solar-energy systems in Turkey and determine the appropriate solar technology.     

Wind resource assessment of an area using short term data correlated to a long term data set

A method of estimating the annual wind energy potential of a selected site using short term measurements related to one year’s recorded wind data at another reference site is presented. The proposed method utilizes the 1-year measured wind speed of one site to extrapolate the annual wind speed at a new site, using an artificial neural network (ANN). In this study, concurrent measurements from target and reference sites over periods of 1-month and 2-month were used to “train” the ANN. Topographical details or other meteorological data are not required for this approach. After derivation of the simulated wind speed time series for the target site, its mean value and its corresponding Weibull distribution parameters are calculated. The derived Weibull distribution of the simulated wind speed is used to make an assessment of the annual wind energy resource in the new area with respect to a particular wind turbine model. Three pairs of measuring stations in the southwest of Ireland were examined, where the wind potential is high and technically exploitable. Analysis of the measurements showed a reasonable cross-correlation coefficient of the wind speed between the sites. Results indicate that with this method, only a short time period of wind data acquisition in a new area might provide the information required for a satisfactory assessment of the annual wind energy resource. To evaluate the accuracy of the method, simulation results of the 1-month and 2-month training periods are compared to the corresponding actual values recorded at the sites. Also, a comparison with the results of a commercial wind energy assessment software package is presented showing similar results.     

Wind energy potential in Antakya and skenderun regions, Turkey

The aim of this study is to establish the potential and the feasibility basis for the wind energy resources in some locations of East Mediterranean region of Turkey and provide suitable data for evaluating the potential wind power. For this purpose, hourly wind data, which were observed between the years 1997 and 2001 at the meteorological stations of Antakya and skenderun regions, were used. The dominant wind directions, the mean values, wind speeds, wind potential and the frequency distributions were determined. The results were classified according to the height above the ground level. Finally, the wind atlas of these regions in the form of contours of constant wind speed and wind potential was produced.     

A neural network approach to local downscaling of GCM output for assessing wind power implications of climate change

A methodology is presented for downscaling General Circulation Model (GCM) output to predict surface wind speeds at scales of interest in the wind power industry under expected future climatic conditions. The approach involves a combination of Neural Network tools and traditional weather forecasting techniques. A Neural Network transfer function is developed to relate local wind speed observations to large scale GCM predictions of atmospheric properties under current climatic conditions. By assuming the invariability of this transfer function under conditions of doubled atmospheric carbon dioxide, the resulting transfer function is then applied to GCM output for a transient run of the National Center for Atmospheric Research coupled ocean-atmosphere GCM. This methodology is applied to three test sites in regions relevant to the wind power industry—one in Texas and two in California. Changes in daily mean wind speeds at each location are presented and discussed with respect to potential implications for wind power generation.     

Assessment of solar and wind energy resources in Ethiopia. II. Wind energy

Using wind data from 21 meteorological stations with hourly or 3-hourly readings and 60 stations with monthly means, together with data from previous studies of neighbouring countries, a series of analyses were undertaken to illustrate the general availability of wind energy across Ethiopia. In order to calculate the wind energy density, firstly these 21 stations, along with 12 stations from neighbouring countries with hourly readings, were used to calculate the Weibull parameters, c and k. The Weibull distribution is shown to be a good approximation for the observed values in a majority of cases. Isopleths of the k values were then plotted, and from this the remaining 60 stations with monthly readings were then assigned k values. The wind energy density for each station was then calculated. Although the quantity of wind data is somewhat lacking, the results show that there is a potential for wind energy utilisation in some regions of Ethiopia.     

A study of the wind speed and wind energy availability in Bangladesh

Characteristics of wind speed data for three recent years, recorded at 14 stations of the Bangladesh Meteorological Department, have been studied. The data have been used to compute the monthly average wind speed and the wind energy availability for the stations. Average values of monthly wind speed for 1931–1960 have been employed to obtain the energy availability from the energy pattern factor, and the two sets of results have been compared. It has been found that, for the Chittagong station, the frequency distributions have good fits of the Weibull type.     

Hindcasting hourly wind power across Scotland based on met station data

This paper presents methods and results from a study where long‐term wind measurements at 10 m above ground level from meteorological stations across Scotland were used to hindcast both average and hour‐by‐hour local wind speeds. For this, Scotland was divided into 21 simulation areas each containing a meteorological station. The Wind Atlas Analysis and Application Program (WAsP) was then used—well outside its specified range for both distance and area slope—to predict the wind climate at 80 m above ground level on a square kilometre basis. With further processing, time series of wind speed were derived for selected locations. Based on wind turbine power curves it was then possible to derive time series of power which were applied in power system analysis and used to study the degree of matching between renewable generation and electricity demand. This paper focuses on the creation of the onshore wind speed and power time series for areas of interest in Scotland. Copyright © 2007 John Wiley & Sons, Ltd.