Actual Wind Speed Research Articles

Hybrid Method Based on Random Convolution Nodes for Short-Term Wind Speed Forecasting

Despite having a plethora of works, wind speed time-series forecasting capabilities are prone to errors due to their intermittent and nonstationary nature as well as the limited generalization capabilities of forecasting methods for non-Gaussian distributed data. In this article, a hybrid method that consists of elastic variational mode decomposition (eVMD) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">forecasting</i> random convolution nodes (fRCN) is proposed to forecast the Gaussian heteroscedastic wind speed time-series. The proposed eVMD algorithm gauges the nonstationary characteristics (complexity) of the wind speed signal and thereafter decomposes the signal into its intrinsic components (ICs) accordingly. The fRCN method rely on local receptive fields to extract features that contribute to the local variations and the global trend in each IC. These features are subsequently learned using extreme learning machines theories. An ensemble unit is employed to learn appropriate weightages for each forecasted IC before yielding the final forecasting values. Suitability of the proposed hybrid method for wind speed forecasting is evaluated via an actual wind speed dataset and comparing against various existing hybrid methods.

Application of lidar technology to predict wind resource for modern wind turbines

ABSTRACT Prediction of wind resource potential for a site is crucial to assess any wind farm. However, this can be done by installing long and costly meteorological masts with different wind sensors. This work utilises the application of remote sensing techniques to predict wind resources near the coastal offshore site at Dhanushkodi, near Rameshwaram. This paper is divided into two sections: First, four types of wind speed extrapolating models have been used to predict wind speed at higher heights, the predicted wind speeds are compared with the actual wind speed data recorded by mast and lidar. Second: comparative analyses of Weibull parameters derived from maximum likelihood and modified maximum likelihood methods have been observed using the same extrapolation models at the same heights. The R 2 and RMSE for wind speed and direction at different observed heights were equal to or higher than 0.95 and 0.1426 respectively at Dhanuskhkodi.

Probability Density Forecasting of Wind Speed Based on Quantile Regression and Kernel Density Estimation

Based on quantile regression (QR) and kernel density estimation (KDE), a framework for probability density forecasting of short-term wind speed is proposed in this study. The empirical mode decomposition (EMD) technique is implemented to reduce the noise of raw wind speed series. Both linear QR (LQR) and nonlinear QR (NQR, including quantile regression neural network (QRNN), quantile regression random forest (QRRF), and quantile regression support vector machine (QRSVM)) models are, respectively, utilized to study the de-noised wind speed series. An ensemble of conditional quantiles is obtained and then used for point and interval predictions of wind speed accordingly. After various experiments and comparisons on the real wind speed data at four wind observation stations of China, it is found that the EMD-LQR-KDE and EMD-QRNN-KDE generally have the best performance and robustness in both point and interval predictions. By taking conditional quantiles obtained by the EMD-QRNN-KDE model as the input, probability density functions (PDFs) of wind speed at different times are obtained by the KDE method, whose bandwidth is optimally determined according to the normal reference criterion. It is found that most actual wind speeds lie near the peak of predicted PDF curves, indicating that the probabilistic density prediction by EMD-QRNN-KDE is believable. Compared with the PDF curves of the 90% confidence level, the PDF curves of the 80% confidence level usually have narrower wind speed ranges and higher peak values. The PDF curves also vary with time. At some times, they might be biased, bimodal, or even multi-modal distributions. Based on the EMD-QRNN-KDE model, one can not only obtain the specific PDF curves of future wind speeds, but also understand the dynamic variation of density distributions with time. Compared with the traditional point and interval prediction models, the proposed QR-KDE models could acquire more information about the randomness and uncertainty of the actual wind speed, and thus provide more powerful support for the decision-making work.

Effects of swells on sound propagation in surface duct environment in shallow water

Surface duct is a common duct due to strong sea winds and sea-atmosphere interactions in winter and it is an excellent waveguide in which energy may propagate a long distance. However, the rough interface formed by sea surface waves will seriously damage this excellent performance. In this study, the experimental data of sound propagation over the continental slope in the South China Sea are used to analyze the characteristics of sound propagation in a surface duct. Modeling analyses based on the parabolic equation model RAM and ray trace theory BELLHOP are used to examine these characteristics. The parameters of sea bottom, source depth, wind-driven sea surface, and swell-containing sea surface are taken into consideration in the model. The results show that when the source is located in the surface duct, the parameters of the sea bottom have little influence on sound propagation, while the change of source depth exerts some effects on the sound propagation. By combining the Pierson Moscowitz (PM) spectrum with Monte Carlo method, the rough sea surface is investigated. Since the PM spectrum is related only to wind speed, the wind-driven sea surface is generated by using the actual wind speed measured by the shipborne anemometer. The swell-containing sea surface is defined as a superposition of a sinusoidal pressure-release surface and the wind-driven sea surface. By comparing the effects of two sea surfaces on sound propagation, it is found that when the wind speed is small, swells play an important role in the surface-duct propagation. Experimental data show that for the acoustic signal with a center frequency of &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$1000\;{\rm{Hz}}$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="5-20201549_M3.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="5-20201549_M3.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;, the swell-containing sea surface brings around &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$10 \;{\rm{dB}}$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="5-20201549_M4.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="5-20201549_M4.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt; loss to a distance of &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$70 \;{\rm{km}}$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="5-20201549_M5.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="5-20201549_M5.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;. For the two kinds of rough sea surfaces, rays at launch angles of &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$\pm 1^{\circ}, 0^{\circ}$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="5-20201549_M6.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="5-20201549_M6.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt; are plotted to examine their effects on sound propagation. The results indicate that the swell-containing sea surface which has greater roughness makes rays go toward the sea bottom, thus resulting in larger loss. Therefore, in order to investigate the characteristics of the sound field in the northern South China Sea in winter, especially with high frequency sound signals, the influences of not only winds and waves, but also the swells from the surrounding sea should be taken into consideration. It is important to study the characteristics of sound propagation with swells for improving the performance of sonar equipment in poor sea conditions.

Multistep forecasting for diurnal wind speed based on hybrid deep learning model with improved singular spectrum decomposition

The actual wind speed data suffers from the intermittent and fluctuating property, which implies that it is very difficult to forecast wind speed with high accuracy by applying single or shallow models. Hence, with the purpose of improving the forecasting accuracy and obtain better forecasting results, in this paper, a novel hybrid deep learning model is proposed for multistep forecasting of diurnal wind speed, which is intuitively abbreviated as ISSD-LSTM-GOASVM. Under this formulated model, the improved singular spectrum decomposition (ISSD) is firstly presented to decompose successively the raw wind speed data into several sub-series from high-frequency to low-frequency, which can solve the shortcoming of artificial experience selection of embedding dimension in the original SSD. Then, an effective deep learning model named long short-term memory (LSTM) is adopted to predict the low-frequency sub-series. Meanwhile, deep belief network (DBN) architecture with three hidden layers is constructed to predict the high-frequency sub-series, where hyper-parameters of DBN are determined automatically by grasshopper optimization algorithm (GOA). Finally, the forecasting results of all sub-series are combined to accomplish the final wind speed forecasting. Practical wind speed data from three regions are exploited to evaluate the performance of the proposed model. Case study results indicate that the proposed model for diurnal wind speed has a superior forecasting capability. Moreover, the proposed hybrid model is very competitive compared to the state-of-the-art single model and other hybrid models involved in this paper.

Optimal designing and management of a stand-alone hybrid energy system using meta-heuristic improved sine–cosine algorithm for Recreational Center, case study for Iran country

In this paper, optimal designing and energy management of hybrid photovoltaic/wind/fuel cell (PV/WT/FC) system is presented with cost of hybrid system life span (CHSLS) minimizing and considering loss of load interruption probability (LOLIP) for Recreational Center of Gonbad (RCOG), a remote area region in Iran country using actual irradiance and wind speed data of this region. A meta-heuristic improved sine–cosine algorithm (ISCA) is used based on nonlinearly decreasing inertia weight strategy (NDIWS) for improving global and local search of the conventional SCA to find the optimal combination of hybrid system. In designing problem the optimal size of hybrid system components is determined using ISCA considering CHLS and LOLIP to satisfy the RCOG load demand in most cost-effective way. The performance of the proposed ISCA is compared with conventional SCA and well-known PSO methods in different combinations and under LOLIP changing conditions. Simulation results showed that the ISCA finds easily the optimal combination as PV/WT/FC system with lower CHSLS and better LOLIP. Also WT/FC system is not economical due to high CHSLS for RCOG. The results demonstrated that the average cost of per kW RCOG load supply using the PV/WT/FC for maximum LOLIP of 1% is equal to 0.87$ and for maximum LOLIP of 5% is 0.76$. The results showed the contribution and management of PV, WT and fuel cell together as PV/WT/FC is made a reliable and cost-effective energy system to supply the remote area application like RCOG. Moreover superiority of the ISCA than the SCA and PSO is proved with better convergence speed and accuracy. Also, the effect of some effective factors on the system designing is evaluated.

Exploring Solar and Wind Energy as a Power Generation Source for Solving the Electricity Crisis in Libya

The current study is focused on the economic and financial assessments of solar and wind power potential for nine selected regions in Libya for the first time. As the existing meteorological data, including wind speed and global solar radiation, are extremely limited due to the civil war in the country, it was therefore decided to use the NASA (National Aeronautics and Space Administration) database as a source of meteorological information to assess the wind and solar potential. The results showed that the country has huge solar energy potential compared to wind energy potential. Additionally, it is found that Al Kufrah is a suitable region for the future installation of the Photovoltaic (PV) power plant due to high annual solar radiation. Based on the actual wind speed analysis, Benghazi and Dernah are the best regions for large-scale wind farm installation in the future taking into account existing meteorological data limitations. The values of the wind power density in all regions are considerable and small-scale wind turbines can be used to generate electricity based on NASA average monthly wind data for 37 years (1982–2019). Moreover, this work aimed to evaluate the wind/PV systems technical and economically through RETScreen Expert (Version 6.0, CanmetENERGY Varennes Research Centre of Natural Resources Canada, Varennes, Canada). Focusing on the power supply crisis in the country, the potential of electricity production by 5 kW grid-connected residential/household rooftop PV in all regions is proposed and presented. Additionally, this paper evaluated a techno-economic analysis of the 50MW wind/PV system in suitable places. The performance of a 5 kW and 50 MW PV solar system with three PV technologies, namely mono-crystalline silicon, poly-crystalline silicon, and thin-film (CdTe), was also analyzed. The results demonstrated that the development of the wind/PV system in the selected regions is both technically and economically feasible. The outcomes of this study can help decision-makers in designing and installing PV power plants as an alternative source for the future.

A novel method developed to estimate Weibull parameters

The objective of current work is to compare a newly developed method i.e. Wind Energy Intensification Method (WEIM) with two previously used methods i.e. Maximum Likelihood Method (MLM) and Modified Maximum Likelihood Method (MMLM) to calculate Weibull shape and scale parameters. Three Years (2014–2017) hourly measured wind data at 50 m mast height has been used at sixty locations in Pakistan. These methods have been compared with the help of Wind Energy Error (WEE), RMSE and R2. It has been found that WEIM is the most accurate method while MMLM is the second most accurate. Moreover, a comparison of energy density values estimated using actual wind speed distribution and Weibull distribution with three methods has been conducted, and it is found that WEIM estimates the energy density values which are closest to actual values among three methods at each location. Hence, WEIM can be used with significant accuracy for Weibull parameters determination. Finally, province-wise comparison of energy density values for investigated locations has been done which shows that Chaghi, DI Khan, Rahim Yar Khan and Thatta are the most lucrative sites in each of four provinces.

Marshall–Olkin Power Lomax distribution for modeling of wind speed data

Accurate collection of wind speed records is significant for numerous wind power applications. The present investigation aims to highlight the use of the Marshall–Olkin Power Lomax (MOPLx) distribution for wind speed data. We examine the actual wind speed records gathered from three stations Bahawalpur, Gwadar, and Haripur. The dataset is demonstrated by using MOPLx distribution and compare its modeling performance with renowned probability distributions, for example, Weibull–Lomax, power Lomax, Weibull, power Lindley, Lindley, and Lomax. Findings indicate that MOPLx distribution gives the best fitting as per model evaluation criteria, Akaike information criterion (AIC), Bayesian information criterion (BIC), Kolmogorov Smirnov test (KS), coefficient of determination (R2) and root mean square error (RMSE). Overall, the results demonstrate the feasibility, precision, and effectiveness of the MOPLx distribution for portraying wind speed modeling. It is also observed that the MOPLx model is ideal in terms of the power density error (PDE) criterion.

Short-term wind power prediction based on EMD-LSTM combined model

In recent years, China is actively developing wind power generation. Wind energy is a natural factor with volatility, variability and uncontrollability, which will cause fluctuations in wind farm output. The accurate prediction of wind power is conducive to grid dispatchers deploying scheduling plans or doing ahead of schedule Adjustments to reduce losses to a certain extent are also conducive to improving wind power grid-connected capacity. In this paper, using the advantages of empirical mode decomposition EMD algorithm in nonlinear and non-stationary data processing, a wind farm power prediction model based on EMD-LSTM is established. First, the relevant data is preprocessed to obtain the ideal input sequence, and the LSTM network model is used. The NWP wind speed is corrected first, and the revised forecasted NWP wind speed sequence is closer to the actual wind speed. Then use EMD to decompose the wind power data sequence into data components of different scales, and then use the LSTM long and short-term memory network to model the decomposed IMF components and residual RES respectively, and sum the prediction results of each component and residual As the final prediction result. The research results show that the method described in this paper effectively improves the prediction.

Sistem pemantau iklim mikro pada kandang ayam pedaging tertutup berbasis internet of things

Smart agriculture has an emerged concept by using IoT sensors capable of providing various information about their field condition and conducting environmental monitoring to improve the yield of efficient crops. This research aims to develop a microclimate monitoring system in a closed house. The microclimate being monitored is the effective temperature, which is the temperature felt by broilers at that time in a fast area. In this research, IoT has been implemented using WeMos D1 R32 by sending sensor data to observe the effective temperature parameters as actual temperature, humidity, and wind speed into an MQTT cloud server. Microclimate control in the cage is based on effective temperature. The data can be displayed on a 16x4 LCD screen and accessed via an Android smartphone from anywhere and at any time.

Short-term wind speed prediction based on LMD and improved FA optimized combined kernel function LSSVM

Accurate prediction of wind speed is of great significance to the operation and maintenance of wind farms, the optimal scheduling of turbines, and the safe and stable operation of power grids. A new prediction method for short-term wind speed based on local mean decomposition (LMD) and combined kernel function least squares support vector machine (LSSVM) is proposed. The short-term wind speed time series is decomposed into some components by the LMD algorithm. Based on LSSVM, radial basis function and the Polynomial function are used to generate the combined kernel function. The combined kernel function LSSVM combines the advantages of the radial basis function and the Polynomial function, which can achieve better prediction accuracy. The decomposed wind speed time series are predicted separately by the combined kernel function LSSVM model. At the same time, an improved firefly algorithm is proposed to optimize the parameters of the combined kernel function LSSVM. The final predictive value can be obtained by superimposing the predicted value of each combined kernel function LSSVM prediction model. The actual collected short-term wind speed data is chosen as the research object, the simulation experiments with four prediction horizons have been implemented. Compared with state-of-the-art prediction methods, through the comparison result curve between the prediction and actual wind speed, the box-plot results of predictive error distribution, the comparison results of the relative prediction error, the performance indicators, the Pearson’s test, the DM test and the Taylor diagram results show that the proposed prediction method has higher prediction accuracy and is able to reflect the laws of wind speed correctly. Furthermore, the simulation results of four new datasets and adding noise to the input data of training set show that the proposed prediction method has strong robustness.

A DSM Approach for Distribution Systems with High Wind Power Penetration

The basic objectives of demand side management (DSM) are shifting load from peak hours to off-peak hours and reducing consumption during peak hours. The DSM operation is cleared when deregulated electricity market is considered where the retailer purchases electricity from the electricity market to cover the end users requirements of energy. In this paper, DSM techniques (load shifting and peak clipping) are used to maximize the profit for retailer company by reducing total demand at peak hours and achieve an optimal daily load schedule using linear programing (LP) and genetic algorithm (GA). These techniques are implemented on the 33-bus radial network included wind generation penetration. A short term artificial neural network technique (ANN) is used to get forecasted wind speed and forecasted users load for date 25-March-2018. The ANN uses an actual hourly load data and an actual hourly wind speed data. Then the forecasted data is used in the optimization to get optimal daily load schedule to maximize the profit for retailer company. Finally, comparison is carried out between profit using LP and GA. The optimized DSM succeeded to increase the profits of the company by around 4.5 times its old profit using LP and around 2.5 times using GA.

Research on the Fault Characteristic of Wind Turbine Generator System Considering the Spatiotemporal Distribution of the Actual Wind Speed

A reliable fault monitoring system is one of the conditions that must be considered in the design of large wind farms today. The most important factor for the fault monitoring should be the accurate diagnosis criteria with sensitive fault characteristics. Most of the current fault diagnosis criteria are obtained based on the average wind speed at the center of the hub which is not in accord with the actual wind condition in nature. So, this paper utilizes an equivalent wind speed (EWS), which can describe the actual wind speed spatiotemporal distribution on the rotor disk area considering the effects of wind shear and tower shadow, to analyze the common mechanical and electrical faults again. Firstly, the EWS model applicable to the 3-blade wind turbines is introduced; then the new fault characteristics of the wind turbine rotor aerodynamic imbalance and the stator winding asymmetry are theoretically analyzed based on the EWS model; finally, the simulation platform is built in Matlab/Simulink for comparison and the simulation result is well consistent with the theory analysis. The aim of this research is to find more accurate fault characteristics and help promoting the healthy development of wind power industry.

An Innovative Continuous Power Generation System Comprising of Wind Energy Along With Pumped-Hydro Storage and Open Well

This paper presents an off-grid sustainable power generation system suitable for rural areas of developing countries such as India where open wells are available for the purpose of irrigation and meeting the daily requirements of local families. The proposed scheme comprises of a small wind energy system with pumped-hydro energy storage (PHES), which uses an open well as a source of water. The proposed system is different from all available wind-PHES systems in many ways. It uses two upper reservoirs instead of one; the hydraulic pump gets rotational energy directly from the wind turbine, while the pico-hydro turbine coupled with electric generator gets power from the potential energy of water stored in the upper reservoir. The mathematical model of the system has been developed and the actual wind speed data are used to carry out simulation for evaluation of the model performance. The attractive features of the scheme are its reliability, simplicity, and capability to supply continuous power at constant voltage exactly equal to the rated one in spite of wind speed variations. Another interesting use of the proposed system is to supply water to the locals along with electric power supply.

Multi-time scale wind speed prediction based on WT-bi-LSTM

The accurate and reliable wind speed prediction can benefit the wind power forecasting and its consumption. As a continuous signal with the high autocorrelation, wind speed is closely related to the past and future moments. Therefore, to fully use the information of two direction, an auto-regression model based on the bi-directional long short term memory neural network model with wavelet decomposition (WT-bi-LSTM) is built to predict the wind speed at multi-time scales. The proposed model are validated by using the actual wind speed series from a wind farm in China. The validation results demonstrated that, compared with other four traditional models, the proposed strategy can effectively improve the accuracy of wind speed prediction.

Twin Least Squares Support Vector Regression of Heteroscedastic Gaussian Noise Model

The training algorithm of twin least squares support vector regression (TLSSVR) transforms unequal constraints into equal constraints in a pair of quadratic programming problems, it owns faster computational speed. The classical least squares support vector regression (LSSVR) assumpt that the noise is Gaussian with zero mean and the homoscedastic variance. However, it is found that the noise models in some practical applications satisfy Gaussian distribution with zero mean and heteroscedastic variance. In this paper, the LSSVR is combined with the twin hyperplanes, and then an optimal loss function for Gaussian noise with heteroscedasticity is introduced, which is called the twin least squares support vector regression with heteroscedastic Gaussian noise (TLSSVR-HGN). Like LSSVR, TLSSVR-HGN also lacks sparsity. To analyze the generalization ability of the proposed model, the sparse TLSSVR-HGN (STLSSVR-HGN) is proposed with a simple mechanism. The proposed model has been verified using the artificial data set, several benchmark data sets and actual wind speed data. The experimental results show that TLSSVR-HGN is a better technology than the other algorithms.

Development and Test of a Fringe-Imaging Direct-Detection Doppler Wind Lidar for Aeronautics

DLR currently investigates the use of Doppler wind lidar as sensor within feedforward gust alleviation control loops on fast-flying fixed-wing aircraft. Such a scheme imposes strong requirements on the lidar system such as sub-m/s precision, high rate, high spatial resolution, close measurement ranges and sensitivity to mixed and pure molecular backscatter. We report on the development of a novel direct-detection Doppler wind lidar (DD-DWL) within these requirements. This DD-DWL is based on fringe-imaging of the Doppler-shifted backscatter of UV laser pulses in a field-widened Michelson interferometer using a fast linear photodetector. A prototype for airborne operation has been ground-tested in early 2018 against a commercial coherent DWL, demonstrating its ability of measuring close-range wind speeds with a precision of 0.5 m/s, independent of the actual wind speed.

Dynamic calibration for better SEBALI ET estimations: Validations and recommendations

The widespread usage of the surface balance energy models coupled with remote sensing techniques proved their effectiveness in terms of accurately assessing evapotranspiration rates, crop water requirements as well as crop water usage/productivities. Still, such models require diverse inputs/elements, including climatic, topographic, soil, and remote sensing datasets. The availability of these inputs is questionable and outdated in many developing countries. Thus, an Improved Surface Energy Balance Algorithm for Land (SEBAL), also known as SEBALI, was proposed and adapted for regions lacking soil-related datasets. Still, the usage of a standard calibration method in the surface energy balance models, yielding constant value throughout the cropping season, requires further revision and improvement. In this context, this study proposed a novel dynamic calibration approach to be included within SEBALI, related to the actual wind speed and relative humidity conditions. It was followed by the calibrations and validations of monthly evapotranspiration SEBALI values. Datasets were retrieved randomly from four countries (i.e. Belgium, Germany, Italy and United States) representing different climatic zones, between 2013 and 2014, and based on Eddy Covariance flux towers’ outputs. When calibrated, results showed a Root Mean Square Error (RMSE) of 21.32 mm and an Average of Mean Error (AME) of 15.4 mm between monthly SEBALI outputs and flux towers’ datasets, with an R-squared value of 88.4%. When investigating SEBALI outputs on different buffered zones around the flux towers along a changing Normalized Difference Vegetation Index (NDVI), ET values were poorly correlated (i.e. R-squared lower than 60%) in any buffer zone outside the parcels’ boundary. RMSE showed values larger than 40 mm/month, even at a buffer zone of 250 m. This was related to the diverse Land Use Land Cover (LULC) classes, generating different evapotranspiration rates, found at the boundary of the selected parcels. With the proposed dynamic calibration, the enhanced SEBALI could be then implemented in any agricultural region missing soil-related datasets with high accuracy.

Practical electrical energy production to solve the shortage in electricity in Palestine and Pay Back period

In this paper power energy had been estimated based on actual wind speed records in a coastal city in Palestine Ashdod. The main aims of this study to determine the feasibility of wind turbine and to estimate payback period. Therefore, to encourage investment in renewable energy in Palestine. The daily average wind speed data had been analyzed and fitted to the Weibull probability distribution function. The parameters of Weibull had been calculated by author using Graphical method the applied example wind turbine is 5kw wind turbine generator this is suitable turbine for small scale based on wind speed records on the coastal plain of Palestine. This study calculated the energy that can produce from wind turbine to estimate the revenue of any possible project in wind energy conversion system based on unit area. Energy has been calculated wind energy using two different method based on Weibull data and measured data. The total amount of energy for 2010 is 10749.8 kw.hr/m2 based on measured wind speed. Payback period for the project in wind energy turbines is around 3 years which make the generation electricity possible for small scale but not commercial. This study will lead to assess the wind energy production in Palestine to encourage investment in renewable energy sectors.