Annual Mean Wind Speed Research Articles

A framework for preliminary large-scale urban wind energy potential assessment: Roof-mounted wind turbines

Urban wind energy can provide a decentralized local source of energy for residential areas and reduce the cost of energy by avoiding the losses/costs of long-distance energy transmission. In this perspective, a preliminary assessment of urban wind energy is highly desired by turbine developers, investors and policy makers. However, given the large number of parameters involved, predictions of the wind energy potential in urban areas are very challenging. The present paper, therefore, intends to present a straightforward framework to provide a preliminary and large-scale assessment of the urban wind energy potential, i.e. at city or country scales, for roof-mounted turbines. The framework is based on four main steps: (i) collecting the building data, i.e. the number of potential candidate high-rise buildings and their height and rooftop surface area; (ii) obtaining the annual mean wind speed statistics at the height of these buildings and sorting the building data based on these statistics; (iii) obtaining the turbine characteristics and determining the average number of turbines per building roof; (iv) calculating the annual energy production (AEP). The application of the framework is then illustrated at the country scale for the Netherlands. In this case, the urban wind energy potential is assessed by considering the installation of 18,156 small wind turbines on the roofs of 1513 existing high-rise buildings in 12 major cities in the Netherlands, yielding an annual energy production of 150.1 GWh.

Prioritization of the effectiveness rate of various climatic variables on the annual yield of rain-fed winter wheat using different statistical models

The annual yield of different plant species is affected by various parameters such as soil and water properties, climatic variables, etc. Due to the importance of the role of climatic variables on the yield of rain-fed winter wheat (as a strategic and effective plant species in the food security of the people of Iran), in this research the influence of climatic variables including the mean annual maximum temperature (MAT), the mean annual minimum temperature (MIT), the mean annual sunshine (SS), the mean annual relative humidity (HU), the mean annual wind speed (W) and the annual rainfall (R) on the annual yield of rain-fed winter wheat (AYWW) were evaluated based on the records of 10 stations from 1967 to 2016 scattered in Iran. To evaluate the impact of climate variables on the AYWW, the backward time series regression (BTSR), the backward logistic generalized estimating equation (BLGEE) and the backward generalized estimating equation (BGEE) statistical methods were used. All the statistical methods showed that W and SS variables were respectively the most and the least effective variables on the AYWW. Effective variables on AYWW in the BTSR were found to be the W, MIT and R (in 5, 4 and 1 stations out of 10), Effective variables on AYWW in the BLGEE were found to be the W, MIT, R and MAT (in 5, 3, 1 and 1 stations out of 10) and effective variables on AYWW in the BGEE were found to be the W, MIT, R and HU (in 5, 3, 1 and 1 stations out of 10).

A comparison between Advanced Scatterometer and Weather Research and Forecasting wind speeds for the Japanese offshore wind resource map

AbstractThis paper investigates the validity of the method used in the Japanese offshore wind map (NeoWins) to seamlessly connecting satellite‐derived wind speed for open oceans to mesoscale model‐simulated wind speed for coastal waters. In the NeoWins, the former was obtained from the satellite‐borne Advanced Scatterometer (ASCAT), and the latter was obtained from numerical simulations using the Weather Research and Forecasting (WRF) model. In this study, the consistency of the ASCAT and WRF 10‐m height wind speeds is examined in their overwrapping areas. The comparison between ASCAT and WRF model reveals that their differences in annual mean wind speed are mostly within ±5% and that the ASCAT annual mean wind speed is, as a whole, slightly higher than the WRF annual mean wind speed. The results indicate that there are no large wind speed gaps between WRF and ASCAT in most parts of the Japanese offshore areas. It is moreover found that the discrepancies between the two wind speeds are due to two factors: one is the existence of winter sea ice in the ASCAT observation in the Sea of Okhotsk in ASCAT observation and the other is that the accuracy of the WRF wind speed depends on atmospheric stability.

Load analysis and structural strength evaluation of semi-submersible platform for wind turbines in Jeju Island sea states using hydrodynamic-structure interaction analysis

The present study conducted an analysis of waves and wind on the west coast of Jeju Island in South Korea to derive the loads for a structural analysis under operational, extreme, and survival conditions and then evaluated the structural strength of the DeepCwind semi-submersible platform of NREL for 5 MW wind turbine. As a floating structure is exposed to wind and wave loads in a highly changeable marine environment and consists of tubular joints with slender-element members, a load analysis is necessary for strength evaluation and design of the structure. The wave scatter diagrams based on KORDI’s 24-year measurements of sea states and the annual mean wind speeds were used as the operational conditions. A sea state with a period that is very likely to cause the resonance of a floating structure was applied as the extreme condition. Another sea state during the Bolaven, a typhoon passing west of Jeju Island in 2000, is adopted as the survival condition. The surface pressure distribution of DeepCwind calculate through hydrodynamic analysis was applied to a structural analysis model based on a hydrodynamic-structure interaction analysis. The evaluation of the structural strength is performed in accordance with the provisions of DNV-OS-C201 and API-2A-WSD under operational, extreme, and survival conditions.

Evaluation of Wind Potential for the Generation of Electricity in Aliero, Kebbi State

One of the major developments in the technology today is the wind turbine that generates electricity and feed it directly to the grid which is used in many part of the world. The main purpose of this work is to determine the wind potential for electricity generation in Aliero, Kebbi state. Five years Data (2014-2018) was collected from the metrological weather station (Campell Scientific Model), the equipment installed at Kebbi State University of Science And Technology Aliero The data was converted to monthly and annual averages, and compared with the threshold average wind speed values that can only generate electricity in both vertical and horizontal wind turbines. The highest average wind speed 2.81 m/s was obtained in the month of January and the minimum average wind speed of 1.20 m/s in the month of October. Mean annual wind speed measured in the study area shows that there has been an increase in the wind speed from 2014 which peaked in 2015 and followed by sudden decrease to a minimum seasonal value in the year 2016. The highest wind direction is obtained from the North North-East (NNE) direction. From the results of wind power density it shows that we have highest wind power density in month of January and December with 0.8635 w/ m2 and 0.8295 w/ m2 respectively, while lowest wind power density in the month of October and September with 0.6780 w/ m2 and 0.6575 w/ m2 respectively. Result of the type Wind Turbine to be selected in the study area shows that the site is not viable for power generation using a horizontal wind turbine but the vertical wind turbine will be suitable for the generation of electricity.

Wind energy potential assessment for KPT with a comparison of different methods of determining Weibull parameters

Purpose The purpose of this paper is to investigate wind power potential of site using wind speed, wind direction and other meteorological data including temperature and air density collected over a period of one year. Design/methodology/approach The site-specific air density, wind shear, wind power density, annual energy yield and capacity factors have been calculated at 30 and 10 m above the ground level (AGL). The Weibull parameters have been calculated using empirical, maximum likelihood, modified maximum likelihood, energy pattern and graphical methods to determine the other dependent parameters. The accuracies of these methods are determined using correlation coefficient (R²) and root mean square error (RMSE) values. Findings The site-specific wind shear coefficient was found to be 0.18. The annual mean wind speeds were found to be 5.174 and 4.670 m/s at 30 and 10 m heights, respectively, with corresponding standard deviations of 2.085 and 2.059. The mean wind power densities were found to be 59.50 and 46.75 W/m² at 30 and 10 m heights, respectively. According to the economic assessment, the wind turbine A is capable of producing wind energy at the lowest value of US$ 0.034/kWh. Practical implications This assessment provides the sustainable solution of energy which minimizes the dependence on continuous supply of oil and gas to run the conventional power plants that is a major cause of increasing load shedding in the significant industrial and thickly populated city of Pakistan. Also, this will minimize the quarrel between the local power producer and oil and gas supplier during the peak season. Social implications This wind resource assessment has some important social implications including decreasing the environmental issues, enhancing the uninterrupted supply of electricity and decreasing cost of energy per kWh for the masses of Karachi. Originality/value The results are showing that the location can be used for installing the wind energy power plant at the lower cost per kWh compared to other energy sources. The wind energy is termed as sustainable solution at the lowest cost.

Evaluation of Wind Resource Potential in Mountainous Region: A Case Study of Mandara Mountains

This study analyzed the evaluation of wind resource potential available at Mubi and Gwoza North-eastern Nigeria; and Guider and Mokolo far North Cameroon stations, respectively selected in Mandara Mountains covering period of six years (2011 to 2016). At all stations the overall maximum mean annual wind speed of 2.82 m/s was found at Guider in 2015 and the minimum of 2.20 m/s at Gwoza in 2012. The occurrences of annual mean wind speed of 2.82m/s was observed in 2015 at Guider reflecting a higher Weibull shape of 10.44 and scale 2.96m/s parameter, respectively. They were respectively observed in 2012 in Gwaza as 2.20m/s, 6.34 and 2.36m/s, while others stations are moderate. The annual Wind Power Density (WPD) potential at all stations varies between 7.15 - 14.25 W/m2 while others are moderate. However, it was observed from this evaluation, that Mandara Mountain is a poor wind power potential site based on the International classification, with the highest value being Guider station. Therefore, the site is applicable for wind energy exploitation in small scale.

Projected changes in wind speed and its energy potential in China using a high‐resolution regional climate model

AbstractFollowing its commitment to Paris Agreement in 2015, China has started to explore potential renewable energy solutions with low carbon emissions to mitigate global warming. Though wind energy is one of the most cost‐effective solutions and has been favored for climate policy development around the world, its high sensitivity to climate change raises some critical issues for the long‐term effectiveness in providing sustainable energy supply. Particularly, how wind speed and its energy potential in China will change in the context of global warming is still not well understood. In this paper, we simulate the near‐surface wind speed over China using the PRECIS regional climate modeling system under different RCP emission scenarios for assessing the possible changes in wind speed and wind energy availability over China throughout the 21st century. Overall, the PRECIS model can reasonably reproduce the mesoscale climatological near‐surface wind speed and directions as documented in reanalysis data across most regions of China, while some local discrepancies are reported in the southwestern regions. In the future, the annual mean wind speed would be decreasing in most regions of China, except for a slightly increase in the southeast. The expected changes in wind speed are characterized with different amplitudes and rates under different RCP emission scenarios. The changes in the spatial distribution of wind speed seem to be sensitive for RCP climate emission scenarios, especially in the late 21st century. The spatiotemporal changes in wind energy potential exhibit a similar behavior to those in near‐surface wind speed, but the magnitudes of these changes are larger. In general, the wind power density is expected to increase by over 5% in winter in the major wind fields in China (ie, Northwest, Northcentral and Northeast), while significant decreases (by about 6% on average) are projected for other seasons (ie, spring, summer and autumn). By contrast, the wind energy potential in the northeast would increase over most months in the year, especially in winter and summer. The results of this research are of great importance for understanding where and to what extent the wind energy can be utilized to contribute renewable energy system development in China in support of its long‐term climate change mitigation commitment.

Spatiotemporal characteristics of wind energy resources from 1960 to 2016 over China

In the paper, daily near-surface wind speed data from 462 stations are used to study the spatiotemporal characteristics of the annual and seasonal mean wind speed (MWS) and effective wind energy de...

Assessment of weather variability over Safaga harbour, Egypt

Meteorological conditions are key elements in the management of any harbour. The present study aims to present and analyse the meteorological conditions over Safaga Harbour of Egypt. The work is based on meteorological data recorded on hourly basis from January 2007 to December 2017 from the automated weather station in the Harbour. Results revealed a general trend of slight increase in the mean annual air temperature anomaly over the study period with a rate of 0.01 °C/year. The mean annual sea level pressure anomaly also had an increasing trend with a rate of 0.11 hPa/year. The dominant wind directions over the Harbour were N-NW-WNW during the study period. The year 2015 was the year of the lowest annual mean wind speed (9.74 kt), while 2008 was the year of the highest one (13.40 kt). There was a trend of decrease in the mean annual wind speed anomaly with a rate of −0.3 kt/year. Results also revealed a general trend of decrease in the mean annual relative humidity anomaly with a rate of −0.37%/year. Six, seven, thirteen and seventeen extreme values were detected for the air temperature, sea level pressure, wind speed and relative humidity, respectively.

Techno‐economic comparison of rooftop‐mounted PVs and small wind turbines: a case study for Brussels

As rooftops in cities are mostly underused, they have a large potential for decentralised electricity production. In that context, photovoltaic (PV) panels have proven to be an effective solution. Meanwhile, the market of small wind turbines is increasing, and some building owners have already installed one or more units on their roof. While the economic comparison between PV panels and wind turbines has already largely been addressed, in general, the space constraint of a rooftop has never been taken into account. In this work, the authors propose a methodology to compare the energy production and the return on investment both for rooftop-mounted PV panels and wind turbines. The comparison is made for relatively tall buildings (≥60 m) with good wind conditions (≥5 m/s annual mean wind speed). Using a brute-force approach, this study presents the results of the methodology applied to a case study: the Brussels Region. On tall rooftops, considering the space already taken by other installations and assuming an average wind speed of 5 m/s, small building-mounted wind turbines are shown to produce more energy than PV panels. Nevertheless, their return on investment is always lower than that of the PV panels.

Spatio-Seasonal Variations in Long-Term Trends of Offshore Wind Speeds Over the Black Sea; an Inter-Comparison of Two Reanalysis Data

Spatio-seasonal variability of long-term trends in mean and 95th percentile wind speeds for the term between 1979 and 2016, over the Black Sea is presented. Our aim is to contribute the existing literature by presenting the inhomogeneous spatial distribution of the long-term trends in both moderate and severe wind speeds on a monthly basis. The analysis is conducted by using two different data; European Centre for Medium-Range Weather Forecasts-ERA-Interim and U.S. National Centers for Environmental Prediction-Climate Forecast System Reanalysis (CFSR) to perform a comparative analysis. The non-parametric Mann–Kendall and Sen’s Slope methods are used to determine the trends and their significance over the Black Sea. CFSR winds presented higher interannual variability than the ERA-Interim. ERA-Interim indicates that annual mean and 95th percentile wind speeds have decreasing trends down to − 0.17%/year and − 0.20%/year in the Sea of Azov, while they have an increasing trend up to 0.35%/year and 0.38%/year in the eastern part, respectively. Results indicate that wind speeds are increasing over 28% ~ 36% of the Black Sea surface area while the wind speeds are decreasing over 2% ~ 4% of the surface area. Pacific North American Oscillation presented an influence almost all over the Black Sea with statistically significant correlation coefficients over 0.5. North Atlantic Oscillation dominates over the southwestern, western and northern Black Sea with inverse correlation coefficients over 0.6. ERA-Interim and CFSR data illustrated a similar distribution pattern over the Black Sea in means of the relation of variations in wind speeds to the teleconnection indices.

Anthropogenic heat fluxes in urban agglomerations and their impact on meteorological processes

One of the main factors of the impact of urbanization on mesoscale atmospheric and climatic processes are the anthropogenic heat fluxes (AHFs) caused by all types of heat sources in urban areas – from industry to metabolism in residents. A calculation of the influence of energy consumption in urban weather and climate made by the COSMO-CLM model with the TERRA-URB scheme shows that anthropogenic heat fluxes have a noticeable effect on urban temperature and wind regime. In Moscow’s agglomeration, the AHF contribution results in an increase of the mean annual temperature by 2°C and of the mean annual wind speed by more than 1 m/s, while the prevailing wind direction changes only slightly.

Spatio-temporal Variations in Aquatic Vegetation Cover and the Potential Influencing Factors in Lake Hongze Based on MODIS Images

Aquatic vegetation is an important part of lake ecosystems and plays a vital role in improving water quality and maintaining biodiversity. At present, China's lakes are facing eutrophication and the degradation of aquatic vegetation. The monitoring of temporal and spatial variations in aquatic vegetation and elucidating the main influencing factors are of great significance for protecting aquatic vegetation and restoring eutrophic lake ecosystems. Therefore, we introduced the Vegetation Present Frequency (VPF) method to extract data on aquatic vegetation and combined this with meteorological factors and human activities to analyze the temporal and spatial in Lake Hongze based on MODIS data from 2007 to 2017. The VPF of aquatic vegetation in Lake Hongze showed clear seasonal and interannual variations. The VPF was significantly higher in spring and summer than in autumn and winter (P<0.05, one way-ANOVA). The maximum VPF of 0.43 occurred in June but the minimum VPF of 0.21 was recorded in January. The VPF from April to October, during the growing season of aquatic vegetation, was significantly higher than in other months. The annual mean VPF of the northern lake area (Z1) decreased significantly (R2=0.56, P<0.01), ranging from the highest value of 0.50 in 2008 to the lowest value of 0.27 in 2016 (a decrease of 45.8%), indicating a significant loss of aquatic vegetation. Spatially, the VPF of Lake Hongze decreases from the coastal zone to the open water, and the VPF values of the northern (Z1) and western sub-lakes (Z2) are higher than that of other lakes segments (Z3-Z5). The interannual variation in VPF for the entire lake was not significantly affected by annual mean temperature, annual precipitation, annual mean wind speed, or annual sunshine duration (P>0.05), indicating that meteorological factors have little influence on interannual variation of aquatic vegetation in this lake. However, total suspended matter concentration was significantly negatively correlated with VPF in Z1 area (R2=0.48, P<0.01), with strong sand-mining activities occurring in this area. These results indicate that the increase of total suspended matter concentrations caused by sand mining is an important driving factor in the decline of aquatic vegetation in the Z1 segment.

Modeling the global distribution of Culicoides imicola: an Ensemble approach

Culicoides imicola is a midge species serving as vector for a number of viral diseases of livestock, including Bluetongue, and African Horse Sickness. C. imicola is also known to transmit Schmallenberg virus experimentally. Environmental and demographic factors may impose rapid changes on the global distribution of C. imicola and aid introduction into new areas. The aim of this study is to predict the global distribution of C. imicola using an ensemble modeling approach by combining climatic, livestock distribution and land cover covariates, together with a comprehensive global dataset of geo-positioned occurrence points for C. imicola. Thirty individual models were generated by ‘biomod2’, with 21 models scoring a true skill statistic (TSS) >0.8. These 21 models incorporated weighted runs from eight of ten algorithms and were used to create a final ensemble model. The ensemble model performed very well (TSS = 0.898 and ROC = 0.991) and indicated high environmental suitability for C. imicola in the tropics and subtropics. The habitat suitability for C. imicola spans from South Africa to southern Europe and from southern USA to southern China. The distribution of C. imicola is mainly constrained by climatic factors. In the ensemble model, mean annual minimum temperature had the highest overall contribution (42.9%), followed by mean annual maximum temperature (21.1%), solar radiation (13.6%), annual precipitation (11%), livestock distribution (6.2%), vapor pressure (3.4%), wind speed (0.8%), and land cover (0.1%). The present study provides the most up-to-date predictive maps of the potential distributions of C. imicola and should be of great value for decision making at global and regional scales.

Projected Changes to Mean and Extreme Surface Wind Speeds for North America Based on Regional Climate Model Simulations

This study evaluates projected changes to surface wind characteristics for the 2071–2100 period over North America (NA), using four Global Environmental Multiscale regional climate model simulations, driven by two global climate models (GCMs) for two Representative Concentration Pathway scenarios. For the current climate, the model simulates well the climatology of mean sea level pressure (MSLP) and associated wind direction over NA. Future simulations suggest increases in mean wind speed for northern and eastern parts of Canada, associated with decreases in future MSLP, which results in more intense low-pressure systems situated in those regions such as the Aleutian and Icelandic Lows. Projected changes to annual maximum 3-hourly wind speed show more spatial variability compared to seasonal and annual mean wind speed, indicating that extreme wind speeds are influenced by regional level features associated with instantaneous surface temperature and air pressure gradients. The simulations also suggest some increases in the future 50-year return levels of 3-hourly wind speed and hourly wind gusts, mainly due to increases in the inter-annual variability of annual maximum values. The variability of projected changes to both extreme wind speed and gusts indicate the need for a larger set of projections, including those from other regional models driven by many GCMs to better quantify uncertainties in future wind extremes and their characteristics.

Evaluation of Wind Power for Electrical Energy Generation in the Mediterranean Coast of Palestine for 14 Years

The generation, distributionand transmission of electricity in Palestine have recently emerged as major issues. This study comprehensively assesses the production of wind energy and the estimation of wind energy potential in Palestine’s south coastal plain. The goal is to evaluate Palestine’s wind energy production by studying wind data and calculating energy and power. This study analyses two actual time series datasets. Findings are elaborated to determine the wind energy conversion per 1 m2. The wind speed data from January 1996 to December 2006 in Gaza and from January 2012 to December 2015 in Ashqelon are selected as the data sample. This study is crucial given the need for clean and renewable energy, the power shortage in the Gaza Strip and the limited number of wind energy studies conducted in the south coastal plain of Palestine, especially Gaza Strip. This study estimates the wind energy potential of the Gaza Strip to determine the wind potential. The annual mean wind speed and power are 4.11 ms-1 and 903.4 Wm-2, respectively. Moreover, the study clarifies the electrical energy generation in the Gaza Strip using small-scale turbines and offers a feasible alternative to existing schemes.

Wind resource assessment and economic analysis for electricity generation in three locations of the Republic of Djibouti

In the Republic of Djibouti, due to increasing electricity demands, the government has planned to increase power supply by using renewable resources such as geothermal, solar and wind energy. This work presents the first wind resource assessment in the Republic of Djibouti using measured wind speed data for the period of three years by meteorological stations at eight locations. The results confirmed that three of the eight locations (i.e. GaliMa-aba, Ghoubbet and Bada Wein) have the best resource, with mean annual wind speeds of more than 6.0 m/s. Wind simulations using NCEP-CFSR and ERA5 models reanalysis shows that the seasonal variations are stable between different years and are broadly consistent with the observed wind speed.The feasibility of three wind farms with total capacity of 275 MW at GaliMa-aba, Ghoubbet and Bada Wein is examined. Using the WindPRO program and two commercial wind turbines in according to IEC 61400-1 design criteria, the electricity generation were technically assessed. These wind farms can produce 1073 GWh of electricity per year, approximately equal to Djibouti's expected average annual electrical demand in 2030. The economic evaluation using the present value cost (PVC) method estimate that the generation cost per kWh at these locations varies from 7.03 US.$ cent/kWh to 9.67 US.$ cent/kWh.

Wind Data Analysis, Annual Resource Estimation And Comparison With Measured Annual Energy Yield at the University Wind Turbine

Evaluation of the power potential of a particular type of wind turbine at a specific site is necessary for economic decisions. Therefore, the information of a wind turbine and that of a site have to be measured or predicted and then combined with the power curve of a wind turbine. The main objective of this research was to predict the power potential of the existing small wind turbine with a diameter of 3m and the wind turbine site at the University of Siegen and compare with the annual energy calculated from the measured one year of wind and turbine data. Techniques for prediction of the wind speed distribution of a site were determined and modeled. The power curve of the wind turbine was modeled from data recorded by applying a technique from the novel methods for modelling the power curve. In this research, artificial neural network, Weibull and Rayleigh are the techniques modeled to predict wind speed distribution at the wind turbine site. Rayleigh and Weibull were chosen since the two models depict a better wind speed distribution and require the mean and the standard deviation of the wind speed at the wind turbine site. A neural network trained with the backward propagation levernberg-Marquardt algorithm was applied to predict the wind speed and power potential of the wind turbine site. A comparison between Weibull, Rayleigh and the Levernberg-Marquardt trained neural network wind speed was made. The power curve of the wind turbine was successfully evaluated from wind data and wind turbine data recorded. The results indicate that the annual mean wind speed of the region is 2.54 (m/s) and about 20% of the wind availability was blowing from the west. The annual energy yield predicted from the trained neural network was 372 (kWh) closer to that determined from measured wind speed 360 (kWh) than that determined from Weibull and Rayleigh 337 and 233 (kWh) respectively. The three prediction models are applicable in any region to predict the annual energy of a particular wind turbine site with minimal data available.

Wind Data Analysis, Annual Resource Estimation And Comparison With Measured Annual Energy Yield at the University Wind Turbine

Evaluation of the power potential of a particular type of wind turbine at a specific site is necessary for economic decisions. Therefore, the information of a wind turbine and that of a site have to be measured or predicted and then combined with the power curve of a wind turbine. The main objective of this research was to predict the power potential of the existing small wind turbine with a diameter of 3m and the wind turbine site at the University of Siegen and compare with the annual energy calculated from the measured one year of wind and turbine data. Techniques for prediction of the wind speed distribution of a site were determined and modeled. The power curve of the wind turbine was modeled from data recorded by applying a technique from the novel methods for modelling the power curve. In this research, artificial neural network, Weibull and Rayleigh are the techniques modeled to predict wind speed distribution at the wind turbine site. Rayleigh and Weibull were chosen since the two models depict a better wind speed distribution and require the mean and the standard deviation of the wind speed at the wind turbine site. A neural network trained with the backward propagation levernberg-Marquardt algorithm was applied to predict the wind speed and power potential of the wind turbine site. A comparison between Weibull, Rayleigh and the Levernberg-Marquardt trained neural network wind speed was made. The power curve of the wind turbine was successfully evaluated from wind data and wind turbine data recorded. The results indicate that the annual mean wind speed of the region is 2.54 (m/s) and about 20% of the wind availability was blowing from the west. The annual energy yield predicted from the trained neural network was 372 (kWh) closer to that determined from measured wind speed 360 (kWh) than that determined from Weibull and Rayleigh 337 and 233 (kWh) respectively. The three prediction models are applicable in any region to predict the annual energy of a particular wind turbine site with minimal data available.