In this study, wind energy potential of Siirt
University campus area is statistically examined by using the mean hourly wind speed data between 2014
and 2015 years which are measured by Vantage Pro2 device, located at the roof of the Engineering Faculty building with 6 m altitude. Weibull distribution
function and Rayleigh distribution function are used
as statistical approach to evaluate the wind data. Weibull distribution function is examined by using two different methods that are maximum likelihood estimation and Rayleigh method. The determination
coefficient (R2) and Root Mean Square Error (RMSE) values of these methods are compared. According the error analysis, it is indicated that the Rayleigh method
gives better results. Wind speed and wind power density are calculated in pursuance of Weibull distribution parameters. The results are evaluated as
monthly and annually. Hence, this preliminary study is made to determine the wind energy potential of Siirt University campus area.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
At present, with the development of wind power project in China, there are more and more projects located at the complex terrain and complex environment. At the same time, since the large planned area of project, the complex mountain area, and limited number of met mast, even without met mast, in order to the reliable development of the wind power project, it is important that how to do the wind resource assessment without actual measurement wind data and other conditions such as less reliable wind data, and the met mast was not considered representative. This paper will use the atmospheric model to do mesoscale simulation calculation of wind resources, and then combine with CFD technology to downscaling computation to get high resolution wind power assessment result. Finally, in order to confirm the validity of this application in the actual project, the comparison between calculation values and measurement values is carried out. The verification result through the actual data of different met mast shows that the wind resource assessment method which combines the CFD and mesoscale technologies is reliable. The main contribution of the article is to provide the reference model and approach for regional planning and large scale wind resource assessment when there isn’t enough adequate and effective wind data.
Wind Power Density Analysis for Micro-Scale Wind Turbinestheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
At present, with the development of wind power project in China, there are more and more projects located at the complex terrain and complex environment. At the same time, since the large planned area of project, the complex mountain area, and limited number of met mast, even without met mast, in order to the reliable development of the wind power project, it is important that how to do the wind resource assessment without actual measurement wind data and other conditions such as less reliable wind data, and the met mast was not considered representative. This paper will use the atmospheric model to do mesoscale simulation calculation of wind resources, and then combine with CFD technology to downscaling computation to get high resolution wind power assessment result. Finally, in order to confirm the validity of this application in the actual project, the comparison between calculation values and measurement values is carried out. The verification result through the actual data of different met mast shows that the wind resource assessment method which combines the CFD and mesoscale technologies is reliable. The main contribution of the article is to provide the reference model and approach for regional planning and large scale wind resource assessment when there isn’t enough adequate and effective wind data.
Wind Power Density Analysis for Micro-Scale Wind Turbinestheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation
The two main challenges of predicting the wind speed depend on various atmospheric factors and random variables. This paper explores the possibility of developing a wind speed prediction model using different Artificial Neural Networks (ANNs) and Categorical Regression empirical model which could be used to estimate the wind speed in Coimbatore, Tamil Nadu, India using SPSS software. The proposed Neural Network models are tested on real time wind data and enhanced with statistical capabilities. The objective is to predict accurate wind speed and to perform better in terms of minimization of errors using Multi Layer Perception Neural Network (MLPNN), Radial Basis Function Neural Network (RBFNN) and Categorical Regression (CATREG). Results from the paper have shown good agreement between the estimated and measured values of wind speed.
A WIND POWER PREDICTION METHOD BASED ON BAYESIAN FUSIONcsandit
Wind power prediction (WPP) is of great importance to the safety of the power grid and the
effectiveness of power generations dispatching. However, the accuracy of WPP obtained by
single numerical weather prediction (NWP) is difficult to satisfy the demands of the power
system. In this research, we proposed a WPP method based on Bayesian fusion and multisource
NWPs. First, the statistic characteristics of the forecasted wind speed of each-source
NWP was analysed, pre-processed and transformed. Then, a fusion method based on Bayesian
method was designed to forecast the wind speed by using the multi-source NWPs, which is more
accurate than any original forecasted wind speed of each-source NWP. Finally, the neural
network method was employed to predict the wind power with the wind speed forecasted by
Bayesian method. The experimental results demonstrate that the accuracy of the forecasted
wind speed and wind power prediction is improved significantly.
Wind energy forecasting using radial basis function neural networkseSAT Journals
Abstract
Wind power forecast is essential for a wind farm developer for comprehensive assessment of wind potential at a particular site or
topographical location. Wind energy potential at any given location is a non –linear function of mean average wind speed,
vertical wind profile, energy pattern factor, peak wind speed, prevailing wind direction, lull hours, air density and a few other
parameters. Wind energy pattern data of various locationsis collected from a published resource data book by Centre for Wind
Energy Technology, India.Modeling of wind energy forecasting problem consists of data collection, input-output selection,
mappingand simulation. In this work, artificial neural networks technique is adopted to deal with the wind energy forecasting
problem.After normalization, neural network will be run with training dataset.Radial Basis function based Neural Networks is a
feed-forward algorithm of artificial neural networks that offers supervised learning.It establishes local mapping with two fold
learning quickly.Wind power densities predicted for new locationsare in agreement with the measured values atthewind
monitoring stations.MAPE was found out to be less than 10% for all the values of Wind Power Density predictions at new
topographical locations and R2 is found to be nearer to unity.WPD values are multiplied by wind availability hours (generation
hours) in that particular location to give number of energy units at the turbine output. These values are compared to the output of
the wind turbine model installed in the same region, so as to assess the number of units generated by that particular wind turbine
in the respective locations.This kind of assessment is useful for wind energy projects during feasibility studies. With this work, it is
established that radial basis function neural netscan be used as a diagnostic tool for function approximation problemsconnected
towind energy resourcemodeling& forecast.
Keywords: Wind power density, wind energy, forecast, modeling, air density, peak wind speed, radial basis function,
neural network, CoD, MAPE
Application of swarm intelligence algorithms to energy management of prosumer...IJECEIAES
The paper considers the problem of optimal control of a prosumer with a wind power plant in smart grid. It is shown that control can be performed in non-deterministic conditions due to the impossibility of accurate forecasting of the generation from renewable plants. A control model based on a priority queue of logical rules with structural-parametric optimization is applied. The optimization problem is considered from a separate prosumer, not from the entire distributed system. The solution of the optimization problem is performed by three swarm intelligence algorithms. Computational experiments were carried out for models of wind energy systems on Russky Island and Popov Island (Far East). The results obtained showed the high effectiveness of the swarm intelligence algorithms that demonstrated reliable and fast convergence to the global extreme of the optimization problem under different scenarios and parameters of prosumers. Also, we analyzed the influence of accumulator capacity on the variability of prosumers. The variability, in turn, affects the increase of the prosumer benefits from the interaction with the external global power system and neighboring prosumers.
Short-term load forecasting with using multiple linear regression IJECEIAES
In this paper short term load forecasting (STLF) is done with using multiple linear regression (MLR). A day ahead load forecasting is obtained in this paper. Regression coefficients were found out with the help of method of least square estimation. Load in electrical power system is dependent on temperature, due point and seasons and also load has correlation to the previous load consumption (Historical data). So the input variables are temperature, due point, load of prior day, hours, and load of prior week. To validate the model or check the accuracy of the model mean absolute percentage error is used and R squared is checked which is shown in result section. Using day ahead forecasted data weekly forecast is also obtained.
Using statistical and machine learning techniques to forecast the PV solar power, which can be implemented for: • Managing the economic dispatch, unit commitment, and trading of PV solar power generations with other conventional generations; • Using with situational awareness tools to manage the ramp limitation; Optimal energy management of energy storage systems; • Voltage regulator settings on feeders with PV distributed generation.
In this paper, a new technique has been proposed to solve the trade off common problem in hill climbing search algorithm (HCS) to reach maximum power point tracking (MPPT). The main aim of the new technique is to increase the power efficiency for the wind energy conversion system (WECS). The proposed technique has been combined the three-mode algorithm to be simpler. The novel algorithm is increasing the ability to reach the MPPT without delay. The novel algorithm shows fast tracking capability and enhanced stability under change wind speed conditions.
Wind Speed Data Analysis for Various Seasons during a Decade by Wavelet and S...ijfcstjournal
The prediction of Weather forecasting can be done with the Wind Speed data. In this current paper the concept of using Wavelet and S-transform together for the analysis purpose of Wind data is introduced first time ever. In winter due to low convection process the agitation between wind particles is less. So, the Haar Wavelet is used to detect the discontinuity in the less agitated wind data samples of Winter. But due to abrupt changes in wind data in summer, it is difficult to track the data. So, in that case the concept of the Stransform is introduced.
Algeria engages with determination on the path renewable energies to bring global and long-lasting
solutions to the environmental challenges and to the problems of conservation of the energy resources of
fossil origin. Our study is interested on the wind spinneret which seems one of the most promising with a
very high global growth rate. The object of this article is to estimate the wind deposit of the region of Oran
(Es Senia), important stage in any planning and realization of wind plant. In our work, we began with the
processing of schedules data relative to the wind collected over a period of more than 50 years, to evaluate
the wind potential while determining its frequencies. Then, we calculated the total electrical energy
produced at various heights with three types of wind turbines.The analysis of the results shows that the
wind turbines of major powers allow producing important quantities of energy when we increase the height
of their hubs to take advantage of stronger speeds of wind.
Wireless communication without pre shared secrets using spread spectrum techn...eSAT Journals
Abstract
The wireless communication using spread spectrum relies on the assumption that some secret is shared among source and
destination node before communication or transmission has started. This problem is called the circular dependency problem
(CDP). This CDP exists in large networks, where nodes frequently join and leaves the network. In this work we have introduced
an efficient and reliable mechanism called Advanced Encryption Standard (AES) Algorithm, to overcome circular dependency
problem (CDP). This is an efficient algorithm to make successful transmission of data without pre-sharing any secret key. We
have evaluated this by simulation in Matrix Laboratory (MATLAB).
Keywords: -Spread spectrum, CDP, AES and MATLAB.
Validation of wind resource assessment process based on CFD Jean-Claude Meteodyn
Wind resource assessment requires nowadays more efficient tools to provide an accurate evaluation of production in order to reduce costs.As onshore wind farms are built in more complex terrains, it is necessary to find a new method to provide a fine evaluation of energy which reduces the error during the data extrapolation process. This explains why CFD models have become a standard for WRA in specific conditions.This presentation is focused on the wind speed and energy yield prediction carried out for a 29MW wind farm project. The accuracy of the wind modeling is investigated by the cross validation between the different met masts around the site. The net energy prediction P50 is compared against real wind farm performance data during a blind test organized by EWEA in 2013. More than 50 companies have been involved in order to compare methods results.
Calculating Wind Farm Production in Al-Shihabi (South Of Iraq) Using WASPIJERA Editor
The Wind Turbine farms are becoming popular in the renewable energy world. In this research, the Wind Atlas
Analysis and Application Program (WAsP) has been used to estimate wind power density in Al-Shihabi (south
of Iraq). All statistical operations on data series are obtained from Field data collected from the wind
measurement towers which installed by the Science and Technology Ministry at Kut city south of IRAQ at three
heights (10, 30, 50 m). The wind turbine selected for this study to be installed in the wind farm are Bonus-
300kw, 600kw The Annual Energy Production (AEP) has been calculate which varies between (746.990 -
759.446 MWH) at 30 m and it s varies between produced AEP (1.576 - 1.600 GWh) at 50 m ,this site classified
as ( class-1).
The development of modeling wind speed plays a very important in helping to obtain the actual wind speed data for the benefit of the power plant planning in the future. The wind speed in this paper is obtained from a PCE-FWS 20 type measuring instrument with a duration of 30 minutes which is accumulated into monthly data for one year (2019). Despite the many wind speed modeling that has been done by researchers. Modeling wind speeds proposed in this study were obtained from the modified Rayleigh distribution. In this study, the Rayleigh scale factor (Cr) and modified Rayleigh scale factor (Cm) were calculated. The observed wind speed is compared with the predicted wind characteristics. The data fit test used correlation coefficient (R2), root means square error (RMSE), and mean absolute percentage error (MAPE). The results of the proposed modified Rayleigh model provide very good results for users.
Evaluation of the Energy Performance of the Amougdoul Wind Farm, Morocco IJECEIAES
This paper is concerned with the assessment of the the performance of the Amougdoul wind farm. We have determined the Weibull parameters; namely the scale parameter, c (m/s) and shape parameter, k. After that, we have estimated energy output by a wind turbine using two techniques: the useful power calculation method and the method based on the modeling of the power curve, which is respectively 134.5 kW and 194.19 KW corresponding to 27% and 39% of the available wind energy, which confirm that the conversion efficiency does not exceed 40%.
The two main challenges of predicting the wind speed depend on various atmospheric factors and random variables. This paper explores the possibility of developing a wind speed prediction model using different Artificial Neural Networks (ANNs) and Categorical Regression empirical model which could be used to estimate the wind speed in Coimbatore, Tamil Nadu, India using SPSS software. The proposed Neural Network models are tested on real time wind data and enhanced with statistical capabilities. The objective is to predict accurate wind speed and to perform better in terms of minimization of errors using Multi Layer Perception Neural Network (MLPNN), Radial Basis Function Neural Network (RBFNN) and Categorical Regression (CATREG). Results from the paper have shown good agreement between the estimated and measured values of wind speed.
A WIND POWER PREDICTION METHOD BASED ON BAYESIAN FUSIONcsandit
Wind power prediction (WPP) is of great importance to the safety of the power grid and the
effectiveness of power generations dispatching. However, the accuracy of WPP obtained by
single numerical weather prediction (NWP) is difficult to satisfy the demands of the power
system. In this research, we proposed a WPP method based on Bayesian fusion and multisource
NWPs. First, the statistic characteristics of the forecasted wind speed of each-source
NWP was analysed, pre-processed and transformed. Then, a fusion method based on Bayesian
method was designed to forecast the wind speed by using the multi-source NWPs, which is more
accurate than any original forecasted wind speed of each-source NWP. Finally, the neural
network method was employed to predict the wind power with the wind speed forecasted by
Bayesian method. The experimental results demonstrate that the accuracy of the forecasted
wind speed and wind power prediction is improved significantly.
Wind energy forecasting using radial basis function neural networkseSAT Journals
Abstract
Wind power forecast is essential for a wind farm developer for comprehensive assessment of wind potential at a particular site or
topographical location. Wind energy potential at any given location is a non –linear function of mean average wind speed,
vertical wind profile, energy pattern factor, peak wind speed, prevailing wind direction, lull hours, air density and a few other
parameters. Wind energy pattern data of various locationsis collected from a published resource data book by Centre for Wind
Energy Technology, India.Modeling of wind energy forecasting problem consists of data collection, input-output selection,
mappingand simulation. In this work, artificial neural networks technique is adopted to deal with the wind energy forecasting
problem.After normalization, neural network will be run with training dataset.Radial Basis function based Neural Networks is a
feed-forward algorithm of artificial neural networks that offers supervised learning.It establishes local mapping with two fold
learning quickly.Wind power densities predicted for new locationsare in agreement with the measured values atthewind
monitoring stations.MAPE was found out to be less than 10% for all the values of Wind Power Density predictions at new
topographical locations and R2 is found to be nearer to unity.WPD values are multiplied by wind availability hours (generation
hours) in that particular location to give number of energy units at the turbine output. These values are compared to the output of
the wind turbine model installed in the same region, so as to assess the number of units generated by that particular wind turbine
in the respective locations.This kind of assessment is useful for wind energy projects during feasibility studies. With this work, it is
established that radial basis function neural netscan be used as a diagnostic tool for function approximation problemsconnected
towind energy resourcemodeling& forecast.
Keywords: Wind power density, wind energy, forecast, modeling, air density, peak wind speed, radial basis function,
neural network, CoD, MAPE
Application of swarm intelligence algorithms to energy management of prosumer...IJECEIAES
The paper considers the problem of optimal control of a prosumer with a wind power plant in smart grid. It is shown that control can be performed in non-deterministic conditions due to the impossibility of accurate forecasting of the generation from renewable plants. A control model based on a priority queue of logical rules with structural-parametric optimization is applied. The optimization problem is considered from a separate prosumer, not from the entire distributed system. The solution of the optimization problem is performed by three swarm intelligence algorithms. Computational experiments were carried out for models of wind energy systems on Russky Island and Popov Island (Far East). The results obtained showed the high effectiveness of the swarm intelligence algorithms that demonstrated reliable and fast convergence to the global extreme of the optimization problem under different scenarios and parameters of prosumers. Also, we analyzed the influence of accumulator capacity on the variability of prosumers. The variability, in turn, affects the increase of the prosumer benefits from the interaction with the external global power system and neighboring prosumers.
Short-term load forecasting with using multiple linear regression IJECEIAES
In this paper short term load forecasting (STLF) is done with using multiple linear regression (MLR). A day ahead load forecasting is obtained in this paper. Regression coefficients were found out with the help of method of least square estimation. Load in electrical power system is dependent on temperature, due point and seasons and also load has correlation to the previous load consumption (Historical data). So the input variables are temperature, due point, load of prior day, hours, and load of prior week. To validate the model or check the accuracy of the model mean absolute percentage error is used and R squared is checked which is shown in result section. Using day ahead forecasted data weekly forecast is also obtained.
Using statistical and machine learning techniques to forecast the PV solar power, which can be implemented for: • Managing the economic dispatch, unit commitment, and trading of PV solar power generations with other conventional generations; • Using with situational awareness tools to manage the ramp limitation; Optimal energy management of energy storage systems; • Voltage regulator settings on feeders with PV distributed generation.
In this paper, a new technique has been proposed to solve the trade off common problem in hill climbing search algorithm (HCS) to reach maximum power point tracking (MPPT). The main aim of the new technique is to increase the power efficiency for the wind energy conversion system (WECS). The proposed technique has been combined the three-mode algorithm to be simpler. The novel algorithm is increasing the ability to reach the MPPT without delay. The novel algorithm shows fast tracking capability and enhanced stability under change wind speed conditions.
Wind Speed Data Analysis for Various Seasons during a Decade by Wavelet and S...ijfcstjournal
The prediction of Weather forecasting can be done with the Wind Speed data. In this current paper the concept of using Wavelet and S-transform together for the analysis purpose of Wind data is introduced first time ever. In winter due to low convection process the agitation between wind particles is less. So, the Haar Wavelet is used to detect the discontinuity in the less agitated wind data samples of Winter. But due to abrupt changes in wind data in summer, it is difficult to track the data. So, in that case the concept of the Stransform is introduced.
Algeria engages with determination on the path renewable energies to bring global and long-lasting
solutions to the environmental challenges and to the problems of conservation of the energy resources of
fossil origin. Our study is interested on the wind spinneret which seems one of the most promising with a
very high global growth rate. The object of this article is to estimate the wind deposit of the region of Oran
(Es Senia), important stage in any planning and realization of wind plant. In our work, we began with the
processing of schedules data relative to the wind collected over a period of more than 50 years, to evaluate
the wind potential while determining its frequencies. Then, we calculated the total electrical energy
produced at various heights with three types of wind turbines.The analysis of the results shows that the
wind turbines of major powers allow producing important quantities of energy when we increase the height
of their hubs to take advantage of stronger speeds of wind.
Wireless communication without pre shared secrets using spread spectrum techn...eSAT Journals
Abstract
The wireless communication using spread spectrum relies on the assumption that some secret is shared among source and
destination node before communication or transmission has started. This problem is called the circular dependency problem
(CDP). This CDP exists in large networks, where nodes frequently join and leaves the network. In this work we have introduced
an efficient and reliable mechanism called Advanced Encryption Standard (AES) Algorithm, to overcome circular dependency
problem (CDP). This is an efficient algorithm to make successful transmission of data without pre-sharing any secret key. We
have evaluated this by simulation in Matrix Laboratory (MATLAB).
Keywords: -Spread spectrum, CDP, AES and MATLAB.
Validation of wind resource assessment process based on CFD Jean-Claude Meteodyn
Wind resource assessment requires nowadays more efficient tools to provide an accurate evaluation of production in order to reduce costs.As onshore wind farms are built in more complex terrains, it is necessary to find a new method to provide a fine evaluation of energy which reduces the error during the data extrapolation process. This explains why CFD models have become a standard for WRA in specific conditions.This presentation is focused on the wind speed and energy yield prediction carried out for a 29MW wind farm project. The accuracy of the wind modeling is investigated by the cross validation between the different met masts around the site. The net energy prediction P50 is compared against real wind farm performance data during a blind test organized by EWEA in 2013. More than 50 companies have been involved in order to compare methods results.
Calculating Wind Farm Production in Al-Shihabi (South Of Iraq) Using WASPIJERA Editor
The Wind Turbine farms are becoming popular in the renewable energy world. In this research, the Wind Atlas
Analysis and Application Program (WAsP) has been used to estimate wind power density in Al-Shihabi (south
of Iraq). All statistical operations on data series are obtained from Field data collected from the wind
measurement towers which installed by the Science and Technology Ministry at Kut city south of IRAQ at three
heights (10, 30, 50 m). The wind turbine selected for this study to be installed in the wind farm are Bonus-
300kw, 600kw The Annual Energy Production (AEP) has been calculate which varies between (746.990 -
759.446 MWH) at 30 m and it s varies between produced AEP (1.576 - 1.600 GWh) at 50 m ,this site classified
as ( class-1).
The development of modeling wind speed plays a very important in helping to obtain the actual wind speed data for the benefit of the power plant planning in the future. The wind speed in this paper is obtained from a PCE-FWS 20 type measuring instrument with a duration of 30 minutes which is accumulated into monthly data for one year (2019). Despite the many wind speed modeling that has been done by researchers. Modeling wind speeds proposed in this study were obtained from the modified Rayleigh distribution. In this study, the Rayleigh scale factor (Cr) and modified Rayleigh scale factor (Cm) were calculated. The observed wind speed is compared with the predicted wind characteristics. The data fit test used correlation coefficient (R2), root means square error (RMSE), and mean absolute percentage error (MAPE). The results of the proposed modified Rayleigh model provide very good results for users.
Evaluation of the Energy Performance of the Amougdoul Wind Farm, Morocco IJECEIAES
This paper is concerned with the assessment of the the performance of the Amougdoul wind farm. We have determined the Weibull parameters; namely the scale parameter, c (m/s) and shape parameter, k. After that, we have estimated energy output by a wind turbine using two techniques: the useful power calculation method and the method based on the modeling of the power curve, which is respectively 134.5 kW and 194.19 KW corresponding to 27% and 39% of the available wind energy, which confirm that the conversion efficiency does not exceed 40%.
An Experimental Study of Weibull and Rayleigh Distribution Functions of Wind ...TELKOMNIKA JOURNAL
This paper compares two commonly used functions, the Weibull and Rayleigh distribution
functions, for fitting a measured wind speed probability distribution at a given location over a certain period.
The monthly and annual measured wind speed data at 84 m height for the years have been statistically
analyzed for the country with a large capacity - Kitka. The analysis is made in the case of the
implementation of all the predicted capacity of wind turbines and by virtue of the probability of power
distribution. The Weibull and Rayleigh probability distribution functions have been determined and their
parameters have been identified. The average wind speed and the wind power density have been
estimated using both distribution functions and compared those estimated from the measured probability
distribution function. The Weibull distribution function fits the wind speed variation better than Rayleigh
distribution function. The average wind speed was found to be 4.5 m/s and the average wind power
density was 114.54 W/m According to results, we can conclude that such a distribution of winds in this
region yields an appropriate average value of wind power.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Estimation of global solar radiation by using machine learning methodsmehmet şahin
In this study, global solar radiation (GSR) was estimated based on 53 locations by using ELM, SVR, KNN, LR and NU-SVR methods. Methods were trained with a two-year data set and accuracy of the mentioned methods was tested with a one-year data set. The data set of each year was consisting of 12 months. Whereas the values of month, altitude, latitude, longitude, vapour pressure deficit and land surface temperature were used as input for developing models, GSR was obtained as output. Values of vapour pressure deficit and land surface temperature were taken from radiometry of NOAA-AVHRR satellite. Estimated solar radiation data were compared with actual data that were obtained from meteorological stations. According to statistical results, most successful method was NU-SVR method. The RMSE and MBE values of NU-SVR method were found to be 1,4972 MJ/m2 and 0,2652 MJ/m2, respectively. R value was 0,9728. Furthermore, worst prediction method was LR. For other methods, RMSE values were changing between 1,7746 MJ/m2 and 2,4546 MJ/m2. It can be seen from the statistical results that ELM, SVR, k-NN and NU-SVR methods can be used for estimation of GSR.
Improved Kalman Filtered Neuro-Fuzzy Wind Speed Predictor For Real Data Set ...IJMER
Wind energy plays an important role as a contributing source of energy, as well as, and in
future. It has become very important to predict the speed and direction in wind farms. Effective wind
prediction has always been challenged by the nonlinear and non-stationary characteristics of the wind
stream. This paper presents three new models for wind speed forecasting, a day ahead, for Egyptian
North-Western Mediterranean coast. These wind speed models are based on adaptive neuro-fuzzy
inference system (ANFIS) estimation scheme. The first proposed model predicts wind speed for one
day ahead twenty four hours based on same month of real data in seven consecutive years. The second
proposed model predicts twenty four hours ahead based only one month of data using a time series
predication schemes. The third proposed model is based on one month of data to predict twenty four
hours ahead; the data initially passed through discrete Kalman filter (KF) for the purpose of
minimizing the noise contents that resulted from the uncertainties encountered during the wind speed
measurement. Kalman filtered data manipulated by the third model showed better estimation results
over the other two models, and decreased the mean absolute percentage error by approximately 64 %
over the first model.
Delineation of Mahanadi River Basin by Using GIS and ArcSWATinventionjournals
Precipitation is the significant segment of hydrologic cycle and this essential wellspring of overflow. In hydrological models precipitation as information, release is mimicked at the outlet of a watershed. Exactness of release re-enactment relies on drainage zone of the watershed. Therefore in the present work Mahanadi river basin lying within Odisha (drainage area approximately 65000 sq. km.) has been delineated in to five subbasins based on the five CWC operated discharge sites in Odisha. In the present work Arc-Swat has been used to delineate the watershed with the help of the (digital elevation model) DEM. At last as indicated by area of release locales, the aggregate study range was isolated into five sub-basins in particular Kesinga, Kantamal, Salebhata, Sundergarh and Tikarpada. It was observed that number of sub-watersheds into which the study area is being depicted relies on number of outlets and density of drainage. For a specific number of outlets, the thick is the density of drainage the more is the quantity of sub-watershed and the other way around.
The quality of data and the accuracy of energy generation forecast by artific...IJECEIAES
The paper presents the issues related to predicting the amount of energy generation, in a particular wind power plant comprising five generators located in south-eastern Poland. Thelocation of wind power plant, the distribution and type of applied generators, and topographical conditions were given and the correlation between selected weather parameters and the volume of energy generation was discussed. The primary objective of the paper was to select learning data and perform forecasts using artificial neural networks. For comparison, conservative forecasts were also presented. Forecasts results obtained shaw that Artificial Neural Networks are more universal than conservative method. However their forecast accuracy of forecasts strongly depends on the selection of explanatory data.
Use of mesoscale modeling to increase the reliability of wind resource assess...Jean-Claude Meteodyn
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There are several available renewable sources of energy, among which Wind Power is the one which is most uncertain in nature. This is because wind speed changes continuously with time leading to uncertainty in availability of amount of wind power generated. Hence, a short-term forecasting of wind speed will help in prior estimation of wind power generation availability for the grid and economic load dispatch.This paper present a comparative study of a Wind speed forecasting model using Artificial Neural Networks (ANN) with three different learning algorithms. ANN is used because it is a non-linear data driven, adaptive and very powerful tool for forecasting purposes. Here an attempt is made to forecast Wind Speed using ANN with Levenberg-Marquard (LM) algorithm, Scaled Conjugate Gradient (SCG) algorithm and Bayesian Regularization (BR) algorithm and their results are compared based on their convergence speed in training period and their performance in testing period on the basis of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE).A 48 hour ahead wind speed is forecasted in this work and it is compared with the measured values using all three algorithms and the best out of the three is selected based on minimum error.
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varying climatic conditions using the Linear, Ridge, Lasso, Smoother, Partial least, KNN
and Gaussian process regression methods. The data of 2002 and 2003 years were used to
obtain regression coefficients of relevant methods. The coefficients were obtained based on
the input parameters. Input parameters were month, altitude, latitude, longitude and landsurface
temperature (LST).The values for LST were obtained from the data of the National
Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer
(NOAA-AVHRR) satellite. Solar radiation was calculated using obtained coefficients in
regression methods for 2004 year. The results were compared statistically. The most
successful method was Gaussian process regression method. The most unsuccessful method
was lasso regression method. While means bias error (MBE) value of Gaussian process
regression method was 0,274 MJ/m2, root mean square error (RMSE) value of method was
calculated as 2,260 MJ/m2. The correlation coefficient of related method was calculated as
0,941. Statistical results are consistent with the literature. Used the Gaussian process
regression method is recommended for other studies.
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Similar to Determination of wind energy potential of campus area of siirt university (20)
Yapay sinir ağı ve noaaavhrr uydu verilerini kullanarak hava sıcaklığının tah...mehmet şahin
iklim değişikliği, çeşitli ısı ve radyasyon akılarının belirlenmesinde, buhar basınç açığı, su potansiyeli, kentsel arazi kullanımı
ve ısı adası, kısa dalga ve uzun dalga radyasyon, stoma direnci, ekoloji, hidroloji ve atmosfer bilimleri de dahil olmak üzere bir
çok uygulama için kullanılmaktadır. Ayrıca, hava sıcaklığı bilgisi insan sağlığı için gereklidir. Bu kadar önemli olan hava
sıcaklığı, meteorolojik istasyonlarda ölçülmektedir. Fakat istasyon dağılımları yeryüzünde yeterli düzeyde olmadığı gibi yeterli
sayıda istasyon da bulunmamaktadır. Bu nedenle, uydular kullanılmaya başlanmıştır. Literatürde yer yüzey sıcaklığı tahmini
yapmak için oldukça fazla algoritma geliştirilmesine rağmen doğrudan hava sıcaklığını tahmin eden algoritmalar yeterince
geliştirilememiştir. Bu nedenle bu çalışmada yapay sinir ağı kullanılarak hava sıcaklığı tahmini yapılmıştır. Yapay sinir ağda
ay, yükseklik, enlem, boylam, aylık ortalama yer yüzey sıcaklıkları girdi olarak kullanılırken, aylık ortalama hava sıcaklığı çıktı
olarak elde edilmiştir. Girdi parametrelerinden yer yüzey sıcaklığı, NOAA/AVHRR datalarından sağlanmıştır. Ağda öğrenme
algoritmaları olarak; tarinlm, trainscg, trainoss kullanılırken transfer fonksiyonu olarak tansig, logsig ve lineer kullanılmıştır.
Ocak 1995’den Aralık 2005’e kadar olarak zaman aralığı, çalışma periyodu olarak belirlenmiştir. Ağın eğitilmesi için 1995-
2004 yılları arası veriler kullanılırken, test verisi olarak 2005 yılı verileri kullanılmıştır. Tahmin sonuçlarının, gerçek datalarla
istatistiksel olarak değerlendirilmesi yapılmış olup hata değeri oldukça az çıkmıştır. El edilen en iyi modellemede, korelasyon
katsayısı ve kök ortalama kare hatası sırasıyla 0.996 ve 1.253 K olarak hesaplanmıştır.
Siirt ilinin yer yüzey sıcaklığının belirlenmesi için farklı split window alg...mehmet şahin
Land surface temperature is an important parameter to control the energy exchange between earth's surface and atmosphere.
In addition, land surface temperature is used in the development of computer modelling of many environmental quantity as
climate change, numerical weather prediction, global water cycle, drought index, solar radiation and frost. In this study,
visible and near infrared channels (channels 1 and 2) and thermal channels (channels 4 and 5) of NOAA / AVHRR satellite
images were used to obtain land surface temperature data. Price-1984, Becker and Li–1990 and Ulivieri et al.-1994 algorithms
were determined to calculate the land surface temperature. Results of land surface temperature obtained from satellite
algorithms were compared in terms of statistics based on the location of Siirt with the actual land surface temperatures
obtained from General Directorate of Meteorology. According to the results; Root Mean Square Error values of Price-1984,
Becker and Li-1990, and Ulivieri et al.-1994 algorithms were calculated as 3.308K, 2.681K and 2.171K, respectively. In the
same order, the correlation coefficients of algorithms were obtained as 0.972, 0974 and 0984. It has been reached to
conclude that using Ulivieri et al.-1994 algorithm is appropriate to estimate land surface temperature that has been obtained
from Ulivieri et al.-1994 algorithm for getting with the lowest error in satellite-based solar energy calculations.
Forecasting of air temperature based on remotemehmet şahin
The aim of this research is to forecast air temperature based on remote sensing data. So, land surface
temperature and air temperature values which were measured by Republic of Turkey Ministry of Forestry and
Water Affairs (Turkish State Meteorological Service) during the period 1995–2001 at seven stations (Adana,
Ankara, Balıkesir, Đzmir, Samsun, Sanlıurfa, Van) were compared. The monthly land surface temperature and
air temperature were used to have correlation coefficients over Turkey. An empirical method was obtained from
equation of correlation coefficients. Separately, Price algorithm was used for the estimation of land surface
temperature values to get air temperatures. Then as statistical, air temperature values, belongs to meteorological
data in Turkey (26–45ºE and 36–42ºN) throughout 2002, were evaluated. The research results showed that
accuracy of estimation of the air temperature changes from 2.453ºK to 2.825ºK by root mean square error.
Estimation of wind power density with artificial neural networkmehmet şahin
Industry and technology are rapidly developing with each passing day. They need energy to sustain this evolution. The demand of energy is mainly provided from fossil fuels. Unfortunately, this kind of energy reserves are consumed away day by day. Therefore, there is a need to use alternative energy sources to supply energy needs. Alternative energy sources can be listed as; solar, wind, wave, biomass, geothermal and hydro-electric power. Our country has significant potential for wind energy. Wind power density estimation is required to determine the wind potential. In this study, the wind power density was estimated by using artificial neural network (ANN) method. Forty meteorological stations were used for ANN training, while eighteen meteorological stations were used to test the trained network. Network has trained according to, respectively; trainlm, trainbfg, trainscg, traincgp traincgb, traincgf ve trainoss learning algorithms. The correlation coefficient (R) and Mean bias error (MBE) of the best developed model were calculated as 0,9767 and -0,3124 W/m2 respectively. Root Mean Square Error (RMSE) was calculated as 1,4786 W/m2. In conclusion, the obtained results demonstrate that the developed model can be used to estimate the wind power density.
Estimation of solar radiation by different machine learning methodsmehmet şahin
In this study, solar radiation was estimated depend on 34 locations. While doing distribution
of the locations on Turkey, they were taken into consideration to be different climatic
conditions. In the study, meteorological data was used between the years of 2007 and 2015. The
meteorological data which were used, were land surface temperature at 5 cm, air temperature,
sunshine duration and solar radiation. As geographical data, locations of latitude, longitude
and altitude were used. Three different methods were used for estimating the solar radiation.
These are the methods of multilayer perceptron neural network, multiple regression and radial
basis function network. The estimation results obtained from the methods were compared with
the actual values obtained from the meteorological station, statistically. The mean bias error
(MBE), root mean square error (RMSE) and correlation coefficient (R) were used as evaluation
criteria. According to statistical results, multilayer perceptron neural networks has given the
most successful result in the three methods. The MBE, RMSE and R values of method were
calculated as 1.5321 MJ/m2, 2.4295 MJ/m2, and 0.9428, respectively. It is recommended to
researchers to use multilayer perceptron neural networks method for studies in this area.
Forecasting long term global solar radiation with an ann algorithmmehmet şahin
and energy-efficient buildings, solar concentrators, photovoltaic-systems and a site-selection of sites for future
power plants). To establish long-term sustainability of solar energy, energy practitioners utilize versatile
predictive models of G as an indispensable decision-making tool. Notwithstanding this, sparsity of solar sites,
instrument maintenance, policy and fiscal issues constraint the availability of model input data that must be
used for forecasting the onsite value of G. To surmount these challenge, low-cost, readily-available satellite
products accessible over large spatial domains can provide viable alternatives. In this paper, the preciseness of
artificial neural network (ANN) for predictive modelling of G is evaluated for regional Queensland, which
employed Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature (LST) as an
effective predictor. To couple an ANN model with satellite-derived variable, the LST data over 2012–2014 are
acquired in seven groups, with three sites per group where the data for first two (2012–2013) are utilised for
model development and the third (2014) group for cross-validation. For monthly horizon, the ANN model is
optimized by trialing 55 neuronal architectures, while for seasonal forecasting, nine neuronal architectures are
trailed with time-lagged LST. ANN coupled with zero lagged LST utilised scaled conjugate gradient algorithm,
and while ANN with time-lagged LST utilised Levenberg-Marquardt algorithm. To ascertain conclusive results,
the objective model is evaluated via multiple linear regression (MLR) and autoregressive integrated moving
average (ARIMA) algorithms. Results showed that an ANN model outperformed MLR and ARIMA models
where an analysis yielded 39% of cumulative errors in smallest magnitude bracket, whereas MLR and ARIMA
produced 15% and 25%. Superiority of an ANN model was demonstrated by site-averaged (monthly) relative
error of 5.85% compared with 10.23% (MLR) and 9.60 (ARIMA) with Willmott's Index of 0.954 (ANN), 0.899
(MLR) and 0.848 (ARIMA). This work ascertains that an ANN model coupled with satellite-derived LST data
can be adopted as a qualified stratagem for the proliferation of solar energy applications in locations that have
an appropriate satellite footprint.
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Determination of wind energy potential of campus area of siirt university
1. DETERMINATION OF WIND ENERGY
POTENTIAL OF CAMPUS AREA OF SIIRT
UNIVERSITY
Nihat Bükün*, Mehmet Şahin+
*, +
Department of Electrical and Electronics Engineering, Siirt University, 56100 Siirt, Turkey
(*nbukun@gmail.com, +
msahin@siirt.edu.tr)
Abstract- In this study, wind energy potential of Siirt
University campus area is statistically examined by
using the mean hourly wind speed data between 2014
and 2015 years which are measured by Vantage Pro2
device, located at the roof of the Engineering Faculty
building with 6 m altitude. Weibull distribution
function and Rayleigh distribution function are used
as statistical approach to evaluate the wind data.
Weibull distribution function is examined by using
two different methods that are maximum likelihood
estimation and Rayleigh method. The determination
coefficient (R2
) and Root Mean Square Error (RMSE)
values of these methods are compared. According the
error analysis, it is indicated that the Rayleigh method
gives better results. Wind speed and wind power
density are calculated in pursuance of Weibull
distribution parameters. The results are evaluated as
monthly and annually. Hence, this preliminary study
is made to determine the wind energy potential of
Siirt University campus area.
Keywords-Weibull distribution, Rayleigh distribution,
maximum likelihood estimation, wind speed, wind
power density.
I. INTRODUCTION
Electrical energy requirement tends to increase
depending on the rapidly advancing technology.
Due to the limited amount of available fossil fuels
used in electricity production and due to the fact
that they will eventually run out, the ways to
conserve electric energy and the use of renewable
energy sources are constantly being studied on. One
of those studies is harvesting wind energy to
generate electric energy which has shown great
development in recent years, especially in Europe.
Turkey is a country with high potential regarding
wind energy. In 2007, Wind Energy Potential Map
of Turkey (REPA) was published [1]. In this map,
wind energy potential of Turkey was provided in
detail for each city. In scope of this study, wind
energy potential of Siirt campus area was studied
and evaluated using the Vantage Pro2 device
located at the roof of Block C of Engineering and
Architecture Faculty, measuring the average hourly
wind speeds between years 2014 and 2015 for a
total of 12 months from 6 meters of altitude. For the
evaluation of wind data, Weibull and Rayleigh
distribution functions were used as statistical
approaches. These two distribution functions are
widely used to determine the wind energy potential
in many studies either in Turkey and other countries
[2]. It is a known fact that wind data usually
matches with Weibull distribution [2-4]. However,
in some areas, wind data does not match with the
two parameter Weibull distribution. But mostly,
Weibull distribution is the method which is used to
represent the wind distribution in many regions
throughout the world. The reason for its use is
because it fits perfectly with the wind distribution
and also has a flexible distribution structure, also,
its parameters can easily be determined and very
few parameters are required. Its parameters can also
easily be estimated for different altitudes, once one
altitude parameters is determined [2].
Wind measurements are usually performed in
the range of 10-30 meters, however, today's large
and powerful wind turbines' hub height is much
higher than this level. Thus, in order to deduct the
value of wind speed in any particular altitude, for
any spot which was measured for just one altitude,
wind power profile law is being used. Weibull
distribution function parameters have been analyzed
using two different methods which are maximum
likelihood estimation (MLE) and Rayleigh method.
Both methods have been compared with coefficient
of determination (R2
) and root mean square error
(RMSE) analysis. Average speed and wind power
density have been statistically determined
depending on the Weibull distribution parameters.
II. WEIBULL DISTRIBUTION
Weibull distribution is used to calculate wind
energy potential in many studies. Wind data is
known to usually fit to this type of distribution.
However, in some areas, wind data does not
conform to the two-parameter Weibull distribution.
Various methods have been developed in order to
calculate the figure (k) and scale (c) parameters.
The methods that we used for Siirt campus area are
the maximum likelihood estimation (MLE) and
Rayleigh method. Likelihood density function of
two parameter Weibull distribution is expressed
with eq.(1).
( ) = ( ) ( ) (1)
Where the wind speed (m/s), k and c are
dimensionless figure and scale parameters,
respectively. The accumulation of Weibull
International Conference on Advanced Technology & Sciences (ICAT’16) September 01-03, 2016 Konya-Turkey
853
_____________________________________________________________________________________________________________
____________________________________________________________________________________________________________
2. distribution (cumulative) likelihood density
function is as in eq.(2) [5].
( ) = 1 − ( ) (2)
Weibull cumulative likelihood density function
gives us the likelihood of the wind speed being
actualized either smaller or equal to a specific v
value. The average wind speed is calculated using
the eq.(3) [5].
= 1 +
1
(3)
Weibull distribution is a function and this
function has a peak spot. Finding this peak means
finding the most probable speed, thus, finding the
maximum wind speed. It is the (y) gamma function
in eq.3 [2].
= (
− 1
) (4)
The speed value which contributes the most to
the energy flow is given in eq.(5) [2]
= (
+ 2
) (5)
The average power density was shown in eq.(6)
[3].
=
1
2
(1 +
3
) (6)
Where, "ρ" is the air density value and in
calculations it was taken as an average of 1,226
kg/m³ for Siirt campus area.Figure parameter (k) is
a parameter indicating the frequency of the wind. If
the wind speed does not show much fluctuation in
an area, and if the wind is blowing with an
approximate constant speed (low or high), it k
parameter is greater. Scale parameter (c) indicates
the relative cumulative wind speed frequency. In
simple words, c parameter changes depending on
the average speed. If the average speed is higher, c
parameter is also higher [3]. Wind speed
measurements are usually made in between an
altitude range of 10 to 30 meters. However,
nowadays, the hubs of wind turbines are much
higher than this level. Therefore, in order to deduct
the wind speed value of any particular altitude of
any location, wind power profile law is being used
together with the measured wind speed data of that
location [6]. Speed values for different altitude are
measured using eq.(7).
( ) = (
ℎ
ℎ
) (7)
In eq.(7), v1 represents the measured wind
speed, v2 represents the desired wind speed, h1
represents the altitude that the v1 speed was
measured, h2 represents the altitude that v2 speed is
demanded to be determined, α represents the
Hellman coefficient and is dependent on the
specifications of the location of the wind speed
measurement is made.
( ) = (
ℎ
ℎ
) (8)
If the power level in the reference altitude can
be found using eq.(8), the power density in the
desired altitude can be calculated also. In the
equation, h1 is the reference altitude, and if the
power density in this altitude is P1 and the power
density in the desired altitude (h2) is represented
with P2.
III. MAXIMUM LIKELIHOOD
ESTIMATION
Maximum likelihood estimation is one of the
methods to find the k and c values which are the
figure and scale parameters of Weibull distribution.
In maximum likelihood estimation, wind data shall
be organized as v1, v2, v3,...........vn, which will form
a set with n number of elements. The likelihood of
any data to be v=vi is proportional with f(vi;k ,c).
Similarly, the likelihood of V=V1…. V= Vn
occurrence of all the data can also be expressed.
These events are independent from each other.
Thus, the likelihood of the occurring of events can
be defined as a likelihood function as in eq.(9) [7].
1 ( ; , )n
iL f V k c (9)
The scale parameter can be obtained using
eq.(10).
=
∑ ( ) (10)
Figure parameter can be calculated using
eq.(11).
=
∑ ln( )
∑ ( )
−
∑ ln( )
(11)
The likelihood function can be used to find the
value that will make the highest likelihood function,
for the k and c parameters calculated using above
given equations. Here, the equation for Vi=0 which
is used for k figure parameter can not be solved.
Therefore, the value of 0 should be removed from
the dataset [2].
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3. IV. RAYLEIGH DISTRIBUTION
The change and distribution of the wind in a
specific period is very important for energy
production evaluations. Turbine designers need
such information like wind distribution and change
in order to make improvements on turbines and to
reduce the costs to a minimum. If in any location,
the only known data is the average wind speed
(Vm); using Rayleigh distribution function, it should
be possible to find the percentage of any specific
wind speed (V) blowing time (hr). The wind speed
values derived from such calculations are a
distribution of likelihood density. When this
distribution is schematically depicted, the region
which is below this distribution equals to 1.
Because the likelihood of the wind blowing in any
speed including zero is equal to 100%. Rayleigh
density function is as it was shown in eq.(12) [2, 5-
10].
( ) =
2
( ) −
2
( ) (12)
Rayleigh cumulative distribution function can
be represented as in eq.(13)
( ) = 1 − −
4
( ) (13)
The biggest advantage of Rayleigh
distribution is that the distribution can be
determined with just using the average wind speed.
In Rayleigh distribution, calculations are done
considering that the k scale parameter equals to 2.
Because the calculations are made over a single
parameter, it is a simpler method compared to
Weibull distribution. Its validity in wind studies
have been shown in many references [2-5].
V. ERROR ANALYSIS
Error analysis shall be done in order to find out
which of the figure and scale parameters calculated
using Weibull distribution, MLE and Rayleigh are
the most suitable ones for the real data. The
methods used in this study have been analyzed
using two different error analysis methods. The first
of these is R2
(determining coefficient) and it can be
expressed as in eq.(14) [2, 8,9].
= 1 −
∑ ( − )
∑ ( − )
(14)
The other error analysis method is root mean
square error (RMSE), and it has been represented in
eq.(15).
=
1
( − )
.
(15)
Where, n is the number of observations, y are
real values, x are values calculated using Weibull
distribution and average real values. The fact that
R2
value is the largest and RMSE value is the
smallest shows that this distribution function is the
best one [2, 8, 9].
VI. RESULTS AND DISCUSSION
In the estimation of the parameters of Weibull
distribution and Rayleigh function, hourly wind
speed data measured in 6 meters of altitude for 12
months between 2014 and 2015 in Siirt campus
area was used. Then the results for 40 meters of
altitude were calculated using Hellmann coefficient.
Table 1. Weibull parameter, speed and power estimates for 2014-2015 data of Siirt campus area
Table 1 shows the analysis of hourly wind
data in Siirt campus. Calculations were performed
with hourly measurements made at an altitude of 6
meters. Then, using Helmann coefficient, wind
power density was calculated for the altitude of 40
meters. As seen in table 1, the highest average
k c
vm
(m/s)
σ
(m/s)
ƒw(ν) Fw(ν)
Vmostlikely
(m/s)
Vmax E
(m/s)
P/A
(w/m2
)
6m
P/A
(w/m2
)
40m
JULY-2014
1.2269 1.9318 1.8002 1.4808 0.6020 0.2489 0.4882 4.2488 3.6179 8.9892
AUGUST 1.1412 1.6777 1.6003 1.4055 0.2620 0.6123 0.2689 4.0741 2.5122 6.2419
SEPTEMBER 1.2241 1.7396 1.6282 1.3371 0.2757 0.6024 0.4345 3.8375 2.6460 6.5744
OCTOBER 1.3019 1.2766 1.1787 0.9131 0.4042 0.5940 0.4154 2.6094 1.0039 2.4943
NOVEMBER 1.1994 1.1068 1.0413 0.8718 0.4227 0.6052 0.2480 2.5080 0.6920 1.7193
DECEMBER 1.7899 0.7339 0.6528 0.3772 0.9882 0.5556 0.4647 1.1160 0.1705 0.4236
JANUARY 2015 1.1195 1.1532 1.1065 0.9901 0.3718 0.6151 0.1563 0.8305 0.8305 2.0685
FEBRUARY 1.1919 1.4217 1.3397 1.1285 0.3265 0.6061 0.3071 3.2491 1.4740 3.6623
MARCH 1.1853 1.5221 1.4364 1.2164 0.3029 0.6069 0.3180 3.5047 1.8168 4.5141
APRIL 1.1440 1.9039 1.8147 1.5902 0.2316 0.6119 0.3110 4.6074 3.6636 9.1065
MAY 1.3280 2.0167 1.8548 1.4103 0.2618 0.5913 0.7036 0.5913 3.9118 9.7195
JUNE 1.3406 2.4522 2.2515 1.6968 0.2177 0.5901 0.8823 4.8456 6.9960 17.3827
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4. speed and power density in Siirt Campus took place
in June. But these two data distributions are not
sufficient for energy investments, it is necessary to
examine the distributions of other data as well.
When we examine the seasonal data, we can see
that the highest average speed and power density
happens to be in summer and late spring months.
Fig.1 shows the power density values calculated in
the years 2014-2015 for Siirt Campus area.
Fig.1. Power densities calculated using MLE for years in between 2014-2015
Depending on the altitude, wind speed and its
power density varies. Parameter values in different
altitudes were deducted from the revised data
obtained for the altitude of 6 meters and by
adapting it to 40 meters of altitude.
Table 2. Rayleigh parameter, speed and power estimates for 2014-2015 data of Siirt campus area
c Vm (m/s)
σ
(m/s)
ƒw(ν) Fw(ν)
Vmostlikely
(m/s)
Vmax E
(m/s)
P/A
(w/m2
)
6m
P/A
(w/m2
)
40m
JULY-2014 2.3422 2.0757 0.7900 0.1508 0.9356 1.6562 3.3124 10.4710 26.0162
AUGUST 2.1796 1.9316 0.7352 0.2027 0.9070 1.5412 3.0824 8.4378 20.9646
SEPTEMBER 2.1141 1.8735 0.7131 0.2263 0.8930 1.4949 2.9897 7.6993 19.1297
OCTOBER 1.5322 1.3578 0.5168 0.4738 0.6908 1.0834 2.1668 2.9309 7.2821
NOVEMBER 1.4548 1.2893 0.4907 0.5049 0.6529 1.0287 2.0574 2.5089 6.2336
DECEMBER 0.7592 0.6728 0.2561 0.5691 0.2504 0.5369 1.0737 0.3566 0.8860
JANUARY 2015 1.6483 1.4607 0.5560 0.4237 0.7429 1.1655 2.3310 3.6490 9.0663
FEBRUARY 1.8286 1.6205 0.6168 0.3436 0.8121 1.2930 2.5860 4.9894 12.3967
MARCH 1.9776 1.7526 0.6671 0.2798 0.8585 1.3984 2.7968 6.3028 15.6599
APRIL 2.4870 2.2040 0.8389 0.1129 0.9546 1.7585 3.5171 12.5343 31.1427
MAY 2.3389 2.0728 0.7889 0.1517 0.9351 1.6539 3.3077 10.4264 25.9054
JUNE 2.7890 2.4717 0.9407 0.0571 0.9795 1.9721 3.9443 17.6790 43.9552
Table 2 shows the analysis according to
Rayleigh distribution of the hourly wind speed data
for Siirt campus area for years 2014-2015.
According to the calculations, the highest power
density was recorded in June and the lowest was
recorded in December.
0
2
4
6
8
10
12
14
16
18
20
PowerDensity(W/m2)
Months
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5. Fig.2. Power densities calculated using Rayleigh for years in between 2014-2015
Fig. 2 shows the power density data calculated
using Rayleigh distribution for Siirt campus area
for years 2014-2015. As can be seen in the figure,
the highest power density was recorded in June and
the lowest was recorded in December. The
coefficient of determination (R2
) is valued either 0
or 1, as the measurement of the power of estimation
of any model. The closer the coefficient of
determination to 1, the higher the power of
estimation of the model used. The smaller the
RMSE value gets, the better that particular
distribution function becomes.
Table 3. The comparison of likelihood distributions calculated using Rayleigh and Weibull distributions
MONTHS METHOD R2 RMSE
July-2014 Weibull 0.87348 0.02658
Rayleigh 0.98975 0.02431
August Weibull 0.90545 0.03017
Rayleigh 0.97552 0.02986
September Weibull 0.92778 0.02653
Rayleigh 0.98993 0.01749
October Weibull 0.99543 0.00994
Rayleigh 0.98847 0.00106
November Weibull 0.91022 0.02452
Rayleigh 0.94961 0.02957
December Weibull 0.91665 0.01065
Rayleigh 0.90463 0.01893
January-2015 Weibull 0.90956 0.03654
Rayleigh 0.90077 0.03012
February Weibull 0.92849 0.02986
Rayleigh 0.91199 0.02901
March Weibull 0.92654 0.02536
Rayleigh 0.99123 0.02014
April Weibull 0.95457 0.03541
Rayleigh 0.98656 0.03210
May Weibull 0.95465 0.02312
Rayleigh 0.99645 0.02017
June Weibull 0.94712 0.03789
Rayleigh 0.99223 0.02875
0
5
10
15
20
25
30
35
40
45
50
PowerDensity(W/m2)
Months
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6. The data obtained from Weibull and Rayleigh
distributions for Siirt campus area for 2014-2015
was presented in Table 3. When the table is
examined, it can be seen that a statistical
comparison of Weibull and Rayleigh distributions
was made according to R2
and RMSE criteria. If
these comparisons are examined, it can be seen that
the use of Rayleigh distribution is more suitable for
that particular region. This can also be seen in Fig.
3. If the figure is examined, it can be seen that the
highest coefficient of determination was obtained
using Rayleigh distribution.
Fig.3. The comparison of Weibull and Rayleigh distributions according to R2
criteria for Siirt campus area in between years 2014 and 2015
VII. CONCLUSIONS
For the determination of the wind potential of
any region for energy purposes, its wind speed
distribution should be known first. Depending on
the wind speed distribution data, wind power
density is calculated and after the required
economic and environmental analysis, it can be
understood if the wind farming would be beneficial
for that particular area or not. In this study, Weibull
parameters used to determine the distribution of
speed have been determined using two different
distribution methods, maximum likelihood
estimation (MLE) and Rayleigh distribution
method. As a result of the error analysis made in
Siirt campus region, and also considering the R2
and RMSE factors, it was seen that Rayleigh
distribution gave better results. The data was
obtained for 6 meters of altitude by using the device
Vantage Pro2 device, located at the roof of the
Engineering Faculty building, and using the
Hellmann coefficient, the possible wind data for the
altitude of 40 meters was calculated. Generally
speaking, when evaluated for its wind potential
throughout 2014-2015, for a period of 12 months, it
has been understood that late Spring and Summer
months had the highest potential of power density,
and the lowest power density was observed in
months of Winter and Fall. For any location to be
eligible to have a wind farm, its power density
should be over 50 W/m2
. In our measurements, we
have come to the conclusion that in order to have a
wind farm in this location, the wind turbine rotors
should be situated above 40 meters of altitude.
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0,8000
0,8200
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0,9000
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Rayleigh
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