This document presents three new models for short-term (24 hours ahead) wind speed forecasting for Egypt's northwestern coast based on real data collected from the site. The first model predicts wind speed using the same month of data from seven consecutive years. The second model predicts using only one month of data with a time series prediction scheme. The third model applies a discrete Kalman filter to one month of data first to reduce noise before prediction using an adaptive neuro-fuzzy inference system (ANFIS). The Kalman filtered data provided more accurate predictions with a 64% reduction in error compared to the first model.
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
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.
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.
Weather forecasting is the application of science and technology to predict the state of the atmosphere for a future time and a given location. Now days, forecasting of accurate atmospheric conditions is the major challenge for the meteorologist and poor forecasting has significant impact on our daily lives. This brings the necessity to make research works on forecasting of the weather events with respect to Ethiopia.
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
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.
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.
Weather forecasting is the application of science and technology to predict the state of the atmosphere for a future time and a given location. Now days, forecasting of accurate atmospheric conditions is the major challenge for the meteorologist and poor forecasting has significant impact on our daily lives. This brings the necessity to make research works on forecasting of the weather events with respect to Ethiopia.
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.
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).
Currently, the quality of wind measure of a site is assessed using Wind Power Density (WPD). This paper proposes to use a more credible metric namely, one we call the Wind Power Potential (WPP). While the former only uses wind speed information, the latter exploits both wind speed and wind direction distributions, and yields more credible estimates. The new measure of quality of a wind resource, the Wind Power Potential Evaluation (WPPE) model, investigates the effect of wind velocity distribution on the optimal net power generation of a farm. Bivariate normal distribution is used to characterize the stochastic variation of wind conditions (speed and direction). The net power generation for a particular farm size and installed capacity are maximized for different distributions of wind speed and wind direction, using the Unrestricted Wind Farm Layout Optimization (UWFLO) methodology. A response surface is constructed, using the recently developed Reliability Based Hybrid Functions (RBHF), to represent the computed maximum power generation as a function of the parameters of the wind velocity (speed and direction) distribution. To this end, for any farm site, we can (i) estimate the parameters of wind velocity distribution using recorded wind data, and (ii) predict the max- imum power generation for a specified farm size and capacity, using the developed response surface. The WPPE model is validated through recorded wind data at four differing stations obtained from the North Dakota Agricultural Weather Network (NDAWN). The results illustrate the variation of wind conditions and, subsequently, its influence on the quality of a wind resource.
Potential Benefits and Implementation of MM5 and RUC2 Data with the CALPUFF A...BREEZE Software
At the absolute minimum, CALMET (CALPUFF’s meteorological preprocessor) requires hourly measurements of surface meteorological data and twice-daily upper air data soundings.
Generating and Using Meteorological Data in AERMOD BREEZE Software
AERMOD, the preferred model of the U.S. EPA for near-field air dispersion modeling, requires the use of two meteorological files: the surface (.SFC) and profile (.PFL) files.
Determination of wind energy potential of campus area of siirt universitymehmet şahin
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.
Optimal combinaison of CFD modeling and statistical learning for short-term w...Jean-Claude Meteodyn
After almost three decades of active research, short-term wind power forecasting is now considered as a mature field. It has been widely and successfully put into operation within the past ten years. Meteodyn with over a decade of experience in wind engineering has contributed to this spread with tens of wind farm equipped with forecast solutions around the world. Our next-generation short-term forecasting solution has been designed to makes the most of both a tailored micro-scale CFD modeling and advanced statistical learning. In the frame of our model design, various options have been considered and evaluated taking into account both model performance and operational constraints. Two main approaches for wind power forecasting are usually considered in the literature (and sometimes opposed): “physical” and “statistical”. It is widely admitted that an optimal combination of both is necessary to build a high performance forecasting system. However, behind "optimal combination" resides a wide variety of design options. We propose here to shed some light on what performances one should expect from several modeling options for combining physics (mesoscale/CFD modeling) and statistics (grey/black box statistical learning, phase/magnitude correction, data filtering). Case studies are taken from real wind farms in various climate and terrain conditions.
2013 ASPRS Track, Developing an ArcGIS Toolbox for Estimating EvapoTranspirat...GIS in the Rockies
Estimating water used by vegetated areas is very important for water resources management and water rights. Traditionally the amount of water delivered to an area is calculated by installing some measuring device (flumes, weirs, flow meters, etc.). The alternative approach presented here estimates the actual water use in a vegetated areas based on ground surface energy balance concept using the ReSET model (Remote Sensing of ET – ReSET developed by IDS group in Colorado state university) that uses satellite and Arial imagery with visible and thermal bands along with weather data to estimate daily actual crop Evapotranspiration (ET) for vegetated areas. Surface energy balance models have been proven to be a robust approach for estimating vegetation evapotranspiration. One of the main limitations of wider application of these models in water resources and irrigation management is the requirement of extensive back ground in surface energy modeling. This presentation shows the development and the application of an ArcGIS toolbox that runs an automated version of the ReSET model. The tool is compatible with NASA/USGS Landsat Legacy Project. The presented ArcGIS tool automates the model in all stages and requires minimum interference from user. The tool presented accommodates both basic and advanced users. The results using the tool were tested and validated using results from manual ReSET model runs.
The PuffR R Package for Conducting Air Quality Dispersion AnalysesRichard Iannone
PuffR is all about helping you conduct dispersion modelling using the CALPUFF modelling system. It is a software package currently being developed using the R statistical programming language. Dispersion modelling is a great tool for understanding how pollutants disperse from sources to receptors, and, how these dispersed pollutants affect populations’ exposure. The presentation goes over basic concepts in air dispersion modelling using CALPUFF, the goals of the project are outlined, the PuffR workflow is described, and a project roadmap is provided.
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.
Sensitivity of AERMOD to AERMINUTE-Generated MeteorologyBREEZE Software
This study presents a comparison of the pollutant concentration predictions from the AERMOD and ISC air dispersion models in the context of fugitive storage tank emissions at a bulk petroleum storage terminal.
Fitting Probability Distribution Functions To Discharge Variability Of Kaduna...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
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.
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).
Currently, the quality of wind measure of a site is assessed using Wind Power Density (WPD). This paper proposes to use a more credible metric namely, one we call the Wind Power Potential (WPP). While the former only uses wind speed information, the latter exploits both wind speed and wind direction distributions, and yields more credible estimates. The new measure of quality of a wind resource, the Wind Power Potential Evaluation (WPPE) model, investigates the effect of wind velocity distribution on the optimal net power generation of a farm. Bivariate normal distribution is used to characterize the stochastic variation of wind conditions (speed and direction). The net power generation for a particular farm size and installed capacity are maximized for different distributions of wind speed and wind direction, using the Unrestricted Wind Farm Layout Optimization (UWFLO) methodology. A response surface is constructed, using the recently developed Reliability Based Hybrid Functions (RBHF), to represent the computed maximum power generation as a function of the parameters of the wind velocity (speed and direction) distribution. To this end, for any farm site, we can (i) estimate the parameters of wind velocity distribution using recorded wind data, and (ii) predict the max- imum power generation for a specified farm size and capacity, using the developed response surface. The WPPE model is validated through recorded wind data at four differing stations obtained from the North Dakota Agricultural Weather Network (NDAWN). The results illustrate the variation of wind conditions and, subsequently, its influence on the quality of a wind resource.
Potential Benefits and Implementation of MM5 and RUC2 Data with the CALPUFF A...BREEZE Software
At the absolute minimum, CALMET (CALPUFF’s meteorological preprocessor) requires hourly measurements of surface meteorological data and twice-daily upper air data soundings.
Generating and Using Meteorological Data in AERMOD BREEZE Software
AERMOD, the preferred model of the U.S. EPA for near-field air dispersion modeling, requires the use of two meteorological files: the surface (.SFC) and profile (.PFL) files.
Determination of wind energy potential of campus area of siirt universitymehmet şahin
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.
Optimal combinaison of CFD modeling and statistical learning for short-term w...Jean-Claude Meteodyn
After almost three decades of active research, short-term wind power forecasting is now considered as a mature field. It has been widely and successfully put into operation within the past ten years. Meteodyn with over a decade of experience in wind engineering has contributed to this spread with tens of wind farm equipped with forecast solutions around the world. Our next-generation short-term forecasting solution has been designed to makes the most of both a tailored micro-scale CFD modeling and advanced statistical learning. In the frame of our model design, various options have been considered and evaluated taking into account both model performance and operational constraints. Two main approaches for wind power forecasting are usually considered in the literature (and sometimes opposed): “physical” and “statistical”. It is widely admitted that an optimal combination of both is necessary to build a high performance forecasting system. However, behind "optimal combination" resides a wide variety of design options. We propose here to shed some light on what performances one should expect from several modeling options for combining physics (mesoscale/CFD modeling) and statistics (grey/black box statistical learning, phase/magnitude correction, data filtering). Case studies are taken from real wind farms in various climate and terrain conditions.
2013 ASPRS Track, Developing an ArcGIS Toolbox for Estimating EvapoTranspirat...GIS in the Rockies
Estimating water used by vegetated areas is very important for water resources management and water rights. Traditionally the amount of water delivered to an area is calculated by installing some measuring device (flumes, weirs, flow meters, etc.). The alternative approach presented here estimates the actual water use in a vegetated areas based on ground surface energy balance concept using the ReSET model (Remote Sensing of ET – ReSET developed by IDS group in Colorado state university) that uses satellite and Arial imagery with visible and thermal bands along with weather data to estimate daily actual crop Evapotranspiration (ET) for vegetated areas. Surface energy balance models have been proven to be a robust approach for estimating vegetation evapotranspiration. One of the main limitations of wider application of these models in water resources and irrigation management is the requirement of extensive back ground in surface energy modeling. This presentation shows the development and the application of an ArcGIS toolbox that runs an automated version of the ReSET model. The tool is compatible with NASA/USGS Landsat Legacy Project. The presented ArcGIS tool automates the model in all stages and requires minimum interference from user. The tool presented accommodates both basic and advanced users. The results using the tool were tested and validated using results from manual ReSET model runs.
The PuffR R Package for Conducting Air Quality Dispersion AnalysesRichard Iannone
PuffR is all about helping you conduct dispersion modelling using the CALPUFF modelling system. It is a software package currently being developed using the R statistical programming language. Dispersion modelling is a great tool for understanding how pollutants disperse from sources to receptors, and, how these dispersed pollutants affect populations’ exposure. The presentation goes over basic concepts in air dispersion modelling using CALPUFF, the goals of the project are outlined, the PuffR workflow is described, and a project roadmap is provided.
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.
Sensitivity of AERMOD to AERMINUTE-Generated MeteorologyBREEZE Software
This study presents a comparison of the pollutant concentration predictions from the AERMOD and ISC air dispersion models in the context of fugitive storage tank emissions at a bulk petroleum storage terminal.
Fitting Probability Distribution Functions To Discharge Variability Of Kaduna...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Impact of Mechanical System in Machining Of AISI 1018 Using Taguchi Design o...IJMER
The imperative objective of the science of metal cutting is the solution of practical problems
associated with the efficient and precise removal of metal from work piece. Optimization of process
parameters is done to have great control over quality, productivity and cost aspects of the process.
Taguchi method stresses the importance of studying the response variation using the signal–to–noise
(S/N) ratio, resulting in minimization of quality characteristic variation due to uncontrollable
parameter. Orthogonal array was adopted in order to planning the (L9) experimental runs in turning of
AISI 1018 by taking the help of software Minitab 16. The MRR and time
Associationship is an important component of data mining. In real world applications, the knowledge that is used for aiding decision-making is always time-varying. However, most of the existing data mining approaches rely on the assumption that discovered knowledge is valid indefinitely. For supporting better decision making, it is desirable to be able to actually identify the temporal features with the interesting patterns or rules. This paper presents a novel approach for mining Efficient Temporal Association Rule (ETAR). The basic idea of ETAR is to first partition the database into time periods of item set and then progressively accumulates the occurrence count of each item set based on the intrinsic partitioning characteristics. Explicitly, the execution time of ETAR is, in orders of magnitude, smaller than those required by schemes which are directly extended from existing methods because it scan the database only once.
The Impacts of Social Networking and Its AnalysisIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Theoretical Analysis for Energy Consumption of a Circulation-Type Superheate...IJMER
Recycled waste material has recently become of interest because of the huge amount of
natural resource consumption worldwide. It is necessary to introduce a material recycle system in
municipal and industrial waste management. Quality improvement of oily metal waste disposed from
metalworking factories as recycling materials is one of the issues. Here, the degreasing system plays an
important role. In this paper, energy consumption of a circulation-type superheated steam degreasing
system was applied to oily metal waste disposed from a metalworking factory. This system was
compared to a once-through type superheated steam degreasing system. Flow rates of materials
applicable to the degreasing system were estimated based on preliminary experiments, and heat and
energy balances from the system were theoretically evaluated and compared between once-through and
circulation type systems. As a result, a circulation-type superheated steam waste degreasing system
that can process oily metal waste provides a promising energy-saving waste metal recycle system.
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.
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.
Contribution to the investigation of wind characteristics and assessment of w...Université de Dschang
M. Bawe Gerard Nfor, Jr. a soutenu sa thèse de Doctorat/Phd en Physique, option Mécanique-Énergétique ce 19 mai 2016 dans la salle des conférences de l'Université de Dschang. A l'issue de la soutenance, le jury présidé par le Prof. Anaclet Fomethe lui a décerné, à l'unanimité de ses membres, la mention très honorable.
Voici la présentation powerpoint qu'il a effectuée dans le cadre de cette soutenance.
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
This paper proposes a Wavelet based Adaptive Neuro-Fuzzy Inference System (WANFIS) applied to forecast the wind power and enhance the accuracy of one step ahead with a 10 minutes resolution of real time data collected from a wind farm in North India. The proposed method consists two cases. In the first case all the inputs of wind series and output of wind power decomposition coefficients are carried out to predict the wind power. In the second case all the inputs of wind series decomposition coefficients are carried out to get wind power prediction. The performance of proposed WANFIS is compared to Wavelet Neural Network (WNN) and the results of the proposed model are shown superior to compared methods.
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%.
WIND SPEED & POWER FORECASTING USING ARTIFICIAL NEURAL NETWORK (NARX) FOR NEW...Journal For Research
Continuous Depleting conventional fuel reserves and its impact as increasing global warming concerns have diverted world attention towards non-conventional energy sources. Out of different non-conventional energy sources wind energy can be consider as one of the cleanest source with minimum possible pollution or harmful emissions and has the potential to decrease the relying on conventional energy sources. Today Wind energy can play a vital role to meet our energy demands; however, it faces various issues such as intermittent nature and frequency instability. To reduce such issues the knowledge of futuristic weather conditions and wind speed trend are required. This work mainly describes the implementation of NARX Artificial neural network for wind speed & power forecasting with the help of historical data available from wind farms.
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.
DEEP LEARNING BASED MULTIPLE REGRESSION TO PREDICT TOTAL COLUMN WATER VAPOR (...IJDKP
Total column water vapor is an important factor for the weather and climate. This study apply
deep learning based multiple regression to map the TCWV with elements that can improve
spatiotemporal prediction. In this study, we predict the TCWV with the use of ERA5 that is the
fifth generation ECMWF atmospheric reanalysis of the global climate. We use an appropriate
deep learning based multiple regression algorithm using Keras library to improve nonlinear
prediction between Total Column water vapor and predictors as Mean sea level pressure, Surface
pressure, Sea surface temperature, 100 metre U wind component, 100 metre V wind component,
10 metre U wind component, 10 metre V wind component, 2 metre dew point temperature, 2
metre temperature.
WATERSHED MODELING USING ARTIFICIAL NEURAL NETWORKS IAEME Publication
Artificial Neural Networks analysis was used for modeling rainfall-runoff relationship. A new Instantaneous ANN watershed model was built and tried herein using Walnut Gulch watershed (catchment) area. For modeling the instantaneous response of a catchment to a rainfall event an ANN model was built shown herein. The built model can represent the actual response using descritized
rainfall-runoff values, over a selected time interval (∆t). As this time interval decreases the actual response is more accurately modeled. This model was applied to one of the sub-catchment of Walnut Gulch watershed (sub-catchment No.9 (flume 11)). The model was found successful to represent the lag-time and time of runoff related to the hyetograph properties
Nonlinear filtering approaches to field mapping by sampling using mobile sensorsijassn
This work proposes a novel application of existing powerful nonlinear filters, such as the standard
Extended Kalman Filter (EKF), some of its variants and the standard Unscented Kalman Filter (UKF), to
the estimation of a continuous spatio-temporal field that is spread over a wide area, and hence represented
by a large number of parameters when parameterized. We couple these filters with the powerful scheme of
adaptive sampling performed by a single mobile sensor, and investigate their performances with a view to
significantly improving the speed and accuracy of the overall field estimation. An extensive simulation work
was carried out to show that different variants of the standard EKF and the standard UKF can be used to
improve the accuracy of the field estimate. This paper also aims to provide some guideline for the user of
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Suitable Wind Turbine Selection using Evaluation of Wind Energy Potential in ...IJCI JOURNAL
Nowadays, low environmental impact of wind energy is attractive. This paper aims to investigate the wind-power production potential of sites in North of Iran. Analysis of the wind speed of one city in the province of MAZANDARAN which is located in north of Iran is performed in this paper. The class of this site is a class one wind power site and the annual average wind speed is 3.58 m/s. The power density of this site is 99 W/m2 at 50 m height. Wind speed data measured over a five-year period at a typical site on the north coast of Iran are presented. The annual wind speeds at different heights have been studied to make optimum selection of wind turbine installation among three commercial turbines
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.
Similar to Improved Kalman Filtered Neuro-Fuzzy Wind Speed Predictor For Real Data Set Collected At Egyptian North-Western Coast (20)
A Study on Translucent Concrete Product and Its Properties by Using Optical F...IJMER
- Translucent concrete is a concrete based material with light-transferring properties,
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surface, depending on the fiber structure. Optical fibers transmit light so effectively that there is
virtually no loss of light conducted through the fibers. This paper deals with the modeling of such
translucent or transparent concrete blocks and panel and their usage and also the advantages it brings
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illumination and to use the optical fiber to sense the stress of structures and also use this concrete as an
architectural purpose of the building
Developing Cost Effective Automation for Cotton Seed DelintingIJMER
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Study & Testing Of Bio-Composite Material Based On Munja FibreIJMER
The incorporation of natural fibres such as munja fiber composites has gained
increasing applications both in many areas of Engineering and Technology. The aim of this study is to
evaluate mechanical properties such as flexural and tensile properties of reinforced epoxy composites.
This is mainly due to their applicable benefits as they are light weight and offer low cost compared to
synthetic fibre composites. Munja fibres recently have been a substitute material in many weight-critical
applications in areas such as aerospace, automotive and other high demanding industrial sectors. In
this study, natural munja fibre composites and munja/fibreglass hybrid composites were fabricated by a
combination of hand lay-up and cold-press methods. A new variety in munja fibre is the present work
the main aim of the work is to extract the neat fibre and is characterized for its flexural characteristics.
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mechanical, properties strictly as per ASTM procedures.
Hybrid Engine (Stirling Engine + IC Engine + Electric Motor)IJMER
Hybrid engine is a combination of Stirling engine, IC engine and Electric motor. All these 3 are
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Fabrication & Characterization of Bio Composite Materials Based On Sunnhemp F...IJMER
The present day technology demands eco-friendly developments. In this era the
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are using as a principle materials. Nowaday the composite materials are utilizing as a important
component of engineering field .Where as the importance of the applications of composites is well
known, but thrust on the use of natural fibres in it for reinforcement has been given priority for some
times. But changing from synthetic fibres to natural fibres provides only half green-composites. A
partial green composite will be achieved if the matrix component is also eco-friendly. Keeping this in
view, a detailed literature surveyed has been carried out through various issues of the Journals
related to this field. The material systems used are sunnhemp fibres. Some epoxy and hardener has
been also added for stability and drying of the bio-composites. Various graphs and bar-charts are
super-imposed on each other for comparison among themselves and Graphs is plotted on MAT LAB
and ORIGIN 6.0 software. To determining tensile strengths, Various properties for different biocomposites
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Geochemistry and Genesis of Kammatturu Iron Ores of Devagiri Formation, Sandu...IJMER
The Greenstone belts of Karnataka are enriched in BIFs in Dharwar craton, where Iron
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major, trace and REE are plotted in various diagrams to interpret the genesis of BIFs. Al2O3, Fe2O3 (T),
TiO2, CaO, and SiO2 abundances and ratios show a wide variation. Ni, Co, Zr, Sc, V, Rb, Sr, U, Th,
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concentrations of Ni, Co and Sc are contributed by chlorite and other components characteristic of basic
volcanic debris. The data suggest that these formations were generated by chemical and clastic
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Experimental Investigation on Characteristic Study of the Carbon Steel C45 in...IJMER
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Non linear analysis of Robot Gun Support Structure using Equivalent Dynamic A...IJMER
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locations. The primary idea employed in this paper, is to model those dynamic loads as equivalent G
force loads in FEA. This approach will be on the conservative side, and will be saving time and
subsequently cost efficient. The approach of the paper is towards creating a standard operating
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Static Analysis of Go-Kart Chassis by Analytical and Solid Works SimulationIJMER
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In récent year various vehicle introduced in market but due to limitation in
carbon émission and BS Séries limitd speed availability vehicle in the market and causing of
environnent pollution over few year There is need to decrease dependancy on fuel vehicle.
bicycle is to be modified for optional in the future To implement new technique using change in
pedal assembly and variable speed gearbox such as planetary gear optimise speed of vehicle
with variable speed ratio.To increase the efficiency of bicycle for confortable drive and to
reduce torque appli éd on bicycle. we introduced epicyclic gear box in which transmission done
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Integration of Struts & Spring & Hibernate for Enterprise ApplicationsIJMER
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complexity. This paper will highlight the design overview of Spring Framework along with its features that
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Microcontroller Based Automatic Sprinkler Irrigation SystemIJMER
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ˆ
s-locally closed sets and spaces are known as
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Intrusion Detection and Forensics based on decision tree and Association rule...IJMER
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Natural Language Ambiguity and its Effect on Machine LearningIJMER
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Today in era of software industry there is no perfect software framework available for
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content and temperature on the stresses in the composite cylinder has been analyzed. The study
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Discrete Model of Two Predators competing for One PreyIJMER
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Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
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Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Vaccine management system project report documentation..pdfKamal Acharya
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Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
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Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Improved Kalman Filtered Neuro-Fuzzy Wind Speed Predictor For Real Data Set Collected At Egyptian North-Western Coast
1. International
OPEN ACCESS Journal
Of Modern Engineering Research (IJMER)
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 4 | Iss.9| Sept. 2014 | 9|
Improved Kalman Filtered Neuro-Fuzzy Wind Speed Predictor For Real Data Set Collected At Egyptian North-Western Coast Mohamed I. Awaad1, Omar M. Salim2, Ossama E. Gouda3, Ebtisam M. Saied4 1,2Electrical Powerand Control Department, BenhaFaulty of Engineering,Benha University, Egypt 3Electrical Power and Machines,Cairo Faculty of Engineering,Cairo University, Egypt 4Electrical power Department, Shoubra Faulty of Engineering,Benha University, Egypt
I. Introduction
Exponential increase in energy demand globally is leading to rapid depletion of existing fossil fuel resources [1], [2]. This has led the power industry to explore renewable energy sources such as wind, solar, and tidal energies. Renewable energy resources attracted more attention recently owing to their pollution free energy generation capabilities. Wind as a potential source for electricity generation on a large scale has been receiving much attention recently. [1], [3] Egypt now relies on burning fossil fuels to satisfy about 85% of its electricity demand, which is growing at a rate of 8% per year[4]. The Arab countries' fossil fuel supply is expected to dry up within the next 30-50 years [4]. The National Renewable Energy Authority (NREA) states that Egypt generated 600 MW of power from wind in 2010 with a goal to generate 7.2 GW of wind power by 2020, about 12% of its total electricity production [4]. Due to the unpredictable nature of wind gust, accurate wind prediction is difficult but much needed. Therefore, researchers have focused on deriving accurate stochastic models for wind speed, wind direction, and consequently wind power prediction. These wind models are based on soft-computing either using probabilistic modeling (using random process estimation theories) or based on approximate reasoning using expert systems like neural networks, fuzzy logic, and hybrid systems [4]. In this paper, new time series forecast models for wind-speed prediction are proposed for Egypt’s north- western coast, since Egypt is a very promising country for wind energy generation. All models are based on real data gathered for that site. The proposed method doesn’t require much data in order to give a prediction with respectable accuracy; the inputs are correlated over severalyears to take into account seasonal changes. The resultant models are used to predict twenty four hours ahead based on same month of real data in seven consecutive years and predicts twenty four hours ahead based only one month of data using a time series predication schemes. The predicted wind-speed is compared to the actualdata to validate the obtained models.
II. Wind-Speed Forecasting
Integration of accurate wind prediction in the management and control regimes involved in the wind energy conversion system (WECS) provides a significant tool for optimizing operating costs and improving
Abstract: 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.
Keywords: Kalman Filtering, Forecasting, State Estimation, Time series, Adaptive Neuro-Fuzzy Inference System.
2. Improved Kalman Filtered Neuro-Fuzzy Wind Speed Predictor For Real Data Set Collected At..
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 4 | Iss.9| Sept. 2014 | 10|
reliability [5]. However, due to highly complex interactions and the contribution of various meteorological
parameters, wind forecasting is a very difficult task. Stochastic techniques depend on collecting wind-speeds data
for wind atlas preparations, wind sites monthly and annual production, and wind turbines optimum sites
prediction using a Weibull statistical model for predicting the performance of hybrid wind systems and their
annual production, consumption of fuel, and costs [4].
Very short-term forecastingis defined as look ahead periods from a few minutes up to an hour, while
short-term forecasting, which is proposed in this paper,will indicate hours out to a few days ahead. This
difference between the two forecasting time periods is important when trying to create a prediction system .Three
main classes of techniques have been identified for wind forecasting. These techniques are numeric weather
prediction (NWP) methods, statistical methods, and methods based upon artificial intelligence [6].
Fuzzy sets were introduced to represent and manipulate data and information that possesses non-statistical
uncertainty. Fuzzy sets are a generalization of conventional set theory that was introduced as a new
way to represent vagueness in the data. It introduces vagueness (with the aim of reducing complexity) by
eliminating the sharp boundary between the members of the class from nonmembers [4].These approaches are
problem dependent to a large extent and converge slowly and even may diverge in certain cases.
Weibulldistribution is the most commonly used probability density function to describe and evaluate the
frequency of wind-speed at the selected sites [4].Weibull distribution can be described by (1) [7];
( )
1 ( ) ( )
w v
v
wB
v w
f w e
(1)
Wherevis a shape parameter, is a scale parameter, and independent variablewis the wind-speed. If the shape
parameter equals 2, the Weibull distribution is known as the Rayleigh distribution. For the Rayleigh distribution
the scale factor, c, given the average wind speed (w ) can be found from (v=2, and
2
w
) [4].
Figure 1. Probability density of the Rayleigh distribution for selected sites
In Fig. 1, the wind-speed probability density function (pdf) of the Rayleigh distribution is plotted. The
average wind speeds in the figure are 5m/s, 5.3 m/s, and 5.4 m/s correspond to the wind speed in SidiBarrani,
MersaMatruh and El Dabaaas three important candidate regions in Egypt’s North-Western coast [7].
In this paper, three different wind-speed prediction models are proposed. The differences between these
models are the size of wind-speed data block required and the scheme by which ANFIS is implemented. All
proposed models are short-term based models, twenty four hours ahead of wind-speed forecasting.
III. Proposed Approach
The proposed approach in this paper is based on Kalman filter and ANFIS as a superior soft-computing
technique. 3.1) Kalman Filter (KF)
The Kalman filter was created by Rudolf E. Kalman in 1960, though Peter Swerling actually developed
a similar algorithm earlier [8]. It was developed as a recursive solution to the discrete-data linear filtering
problem. Kalman filter is based on recursive data processing algorithm and Generates optimal estimate of desired
quantities given the set of measurements.
Kalman Filtering is so popular because Good results in practice due to optimality and structure.
0 5 10 15 20 25
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
Probability density
Wind-speed in m/s
Rayleigh Probability density function pdf
El Dabaa
Mersa Matruh
Sidi Barrani m/s
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In order to use the KF to estimate the internal state of a process given only a sequence of noisy
observations; the following matrices must be specified, which is represented as a linear stochastic difference
equation.
k k k 1 k k k x A x B u w (2)
Where:
k x = state vector
k A = state transition model which is applied to the previous state k 1 x
k B = control-input model which is applied to the control vector uk
k w = process noise which is assumed to be drawn from a zero mean multivariate normal distribution with
covariance Q, P(w) N(0,Q)
The relationship between the process state and the measurementvaluescanbe represented as [8]:
k k k k Z H x v (3)
Where:
k Z = measurement of system state
k H = the observation (or measurement) zk of the true state space into the covariance R
k v = measurement noise; p(v) ~ N(0,R)
To find an equation that computes an a posteriori state estimate as ˆk x a linear combination of an a priori
estimate ˆk x and a weighted difference between the actual measurement k z and a measurement prediction ˆk Hx ,[8].
ˆ ˆ ( ˆ ) k k x x K Z Hx (4)
The difference ( ˆ ) k k z Hx iscalled innovation or residual. Residual of zero;meansthat, the twoterms are
in complete agreement and k is the gain or blending factor thatminimizes the posteriori error covariance.
Matrix k is the gain thatminimizes the a posteriori error covariance. The equationsthatneed to beminimized,
ˆ ˆ ( ˆ ) k k k k x x K Z Hx
(5)
Ongoing Discrete Kalman Filter CycleProject the state ahead,
1 ˆ ˆ k k k k k x A x B u
(6)
Project the error covariance ahead
1
T
k k k k P A P A Q
(7)
Compute the Kalman gain
1 ( ) T T
k k k K P H HP H R (8)
Update estimate with measurement zk
ˆ ˆ ( ˆ ) k k k k k x x K z Hx (9)
Update the error covariance [8]
(1 ) k k k P K H P (10)
3.2) Adaptive Neuro-Fuzzy Inference Systems (ANFIS)
Adaptive Neuro Fuzzy Inference System (ANFIS) is a fuzzy mapping algorithm that is based on Tagaki-Sugeno-
Kang (TSK) fuzzy inference system.ANFIS is integration of neural networks and fuzzy logic and have the
potential to capture the benefits of both these fields in a single framework. ANFIS utilizes linguistic information
from the fuzzy logic as well learning capability of an ANN for automatic fuzzy if-then rule generation and
parameter optimization [9].
A conceptual ANFIS consists of five components: inputs and output database, a Fuzzy system generator,
a Fuzzy Inference System (FIS), and an Adaptive Neural Network. The Sugeno- type Fuzzy Inference System,
which is the combination of a FIS and an Adaptive Neural Network, was used in this study for rainfall-runoff
modeling. The optimization method used is hybrid learning algorithms [9].
For a first-order Sugeno model, a common rule set with two fuzzy if-then rules is as follows:
Rule 1: If x1 is a1 and x1 is b1, then
1 1 1 1 1 1 f a x b x c (11)
Rule 2: If x1 is a2 and x2 is b2, then
2 2 2 2 2 2 f a x b x c (12)
Where, 1 x and 2 x are the crisp inputs to the node and 1 1 2 2 a ,b ,a ,b are fuzzy sets, , i i i a b andc ( i = 1, 2)are the
coefficients of the first-order polynomial linear functions.
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It is possible to assign a different weight toeach rule based on the structure of the system, where, weights
w1 and w2 are assigned to rules 1 and 2 respectively and f = weighted average .
ANFIS network is composed of five consequent layers. Each layer contains several nodes described by
the node function. Let Oi
j
denote the output of the ith node in layer j[9], [10].
In layer 1, every node i is an adaptive node with node function
1 ( ), 1,2 i i O A x i (13)
Or
1
2 ( ), 3,4 i i O B y i (14)
Where x (or y ) is the input to the ith node and i A (or i 2 B ) is a linguistic label associated with this node. The
membership functions for A and B are usually described by generalized bell functions [11], e.g.
2
1
( )
1
i i q
i
i
A x
x r
p
(15)
where{ , , } i i i p q r is the parameter set. Any continuous and piecewise differentiable functions, such as triangular-shaped
membership functions, are also qualified candidates for node functions in this layer [4]. Parameters in this
layer are referred to as premise parameters.
In layer 2, each node multiplies incoming signals and sends the product out
2 ( ) ( ), 1,2 i i i i O w A x B y i (16)
Each node output represents the firing strength of a rule.
In layer 3, each node N computes the ratio of the ith rule firing strength to the sum of all rules’ firing strengths
3
1 2
, 1,2 i
i i
w
O w i
w w
(17)
The outputs of this layer are called normalized firing strengths.In layer 4, each node computes the
contribution of the ith rule to the overall output
4 i i i i ( i i i ), 1,2 O w z w a x b y c i
(18)
Wherewi is the output of layer 3 and {푎푖 , 푏푖 , 푐푖}is the parameter set. Parameters of this layer are referred to as
consequent parameters.
In layer 5, the single node computes the final output as the summation of all incoming signals
5
i i
i
i i i
i i
i
w z
O w z
w
(19)
Thus, an adaptive network is functionally equivalent to a Sugeno-type fuzzy inference system. ANFIS is
an embedded tool in the MATLAB fuzzy toolbox. This approach is based on using the neural networks training
capability to adjust the membership functions’ (MF) parameters of the proposed fuzzy inference system (FIS).
The proposed ANFISs utilize a subtractive clustering technique in which Gaussian MFs are used.
Subtractive clustering generates an initial model for ANFIS training. This subtractive clustering method partitions
the data into groups called clusters, and generates an FIS with the minimum number of rules required to
distinguish the fuzzy qualities associated with each of the clusters. Subtractive clustering avoids the curse of
dimensionality of grid partitioning method. Subtractive clustering is a fast, one-pass algorithm for estimating the
number of clusters and the cluster centers in a set of data. It is especially used if there is no clear idea about how
many clusters should be assigned for a given set of data.
The real-data sets used to build the proposed models were obtained through a huge database website for
weather recordings that covers almost all countries around the globe [12]. These recordings are based on real
hourly-based measurements for the corresponding sites. The study proposed is done for MersaMatruh site as one
of the candidate sites in Egypt that has sufficient wind resources [7].
IV. Simulation Results And Discussions
In this paper the study is based on a real wind speed data gathered from Egypt north-western coast
[12].This location has been selected based on the evaluation done in [7], as it can be considered one of the most
promising locations at the north coast. Each subsection has a model to forecast the wind-speed for a certain period
of time and a different data block size to obtain with four different models by the end of this section.
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1.1. Model-I: 24-Hrs Ahead Based on Yearly Data Recordings
The first model is based on a single month’s wind-speed data in seven consecutive years e.g. the month
of July of years 2007, 2008, 2009, 2010, 2011, 2012 and 2013. In order to train an ANFIS, complete data sets of
inputs along with their corresponding desired output data are needed. Thus, wind-speed data from 2007-2012 are
used as six inputs with data sets of 2013 are used as a corresponding output. Since July is 31 days, only 30 days
data (5040 data points) were used for training and the 31st whole day is to be predicted using the obtained model.
The monthly data is selected in the same season to avoid the climate change between seasons.
1 2 3 4 5 6 Data(k) x x x x x x
(20)
The output training data corresponds to the trajectory prediction.
7 T arget(k) x (21)
Where: 1 x through 7 x are wind-speed data in seven consecutive years e.g. the month of July of years
2007, 2008, 2009, 2010, 2011, 2012 and 2013 respectively.
The training input/output data is a structure whose first component is the six-dimensional input Data(k)
as in (20), and its second component is the output T arget(k) as in (21).
Fig. 2 presents the wind-speed data sets in m/s for July in 2007 through 2013 from upper graph down
respectively, these data sets were obtained from [9]. The data are hourly recordings, thus for 31 days a total of
744 data point are shown per graph, but only 720 points per graph are used for training purposes and the last 24
hours are to be predicted. Fig. 3 shows the generated FIS using subtractive clustering using ANFIS toolbox that
provides a single output Takagi-Sugeno-Kang (TSK) type with linear MFs for the output.
The upper graph of Fig. 4 shows the root mean square error (RMSE) that resulted during the ANN
training epochs. The resultant ANFIS model is used for the purpose of testing and validation to predict 24-Hrs
ahead. The middle graph of Fig. 4 shows the wind-speed forecasting in m/s for one complete day ahead for the
end of July. The error over one day between actual and predicted wind-speed is shown in the lower graph of Fig.
4. The mean value of the error (ME) is found to be around 0.2645 m/s with a mean absolute error (MAE) of
1.6319 m/s. Figure 5 gives scattered plot for actual speed verses predicted speed month data.
Figure 2. Real wind-speed data for 7 months all in July (2007-2013).
0 100 200 300 400 500 600 700
0
10
20
Vw2k7 (m/s)
Wind Speed Readings for seven Complete Months 2007-2013 in m/s
0 100 200 300 400 500 600 700
0
10
20
Vw2k8(m/s)
0 100 200 300 400 500 600 700
0
10
20
Vw2k9 (m/s)
0 100 200 300 400 500 600 700
0
10
20
Vw2k10 (m/s)
0 100 200 300 400 500 600 700
0
10
20
Vw2k11 (m/s)
0 100 200 300 400 500 600 700
0
10
20
Vw2k12 (m/s)
0 100 200 300 400 500 600 700
0
10
20
Vw2k13 (m/s)
Time in hours
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Figure 3. Proposed TSK-FIS for model-I based onReal Wind Speed Data Input
Figure 4. Upper graph is RMSEresult for the model-I, Middel graph is actual and prediction of 24-Hrs ahead
,where the lower graph shows the prediction error
Figure 5. Scattered plot for actual vs predicted month data
1.2. Model-II: 24-Hrs Ahead Based on one month Data
This model is based on only one month wind-speed data, e.g. July 2013. In this model, one month of
hourly based wind-speed recordings are required. Data are rearranged to create a mapping from 4 samples wind-speed
data points, sampled every 24 hours, to a predicted future of 24 hoursas shown in Fig. 6.
Figure 6. Block diagram of ANFIS without Kalman Filter (KF)
Data(k) x(k 3p) x(k 2p) x(k p) x(k)
(22)
0 100 200 300 400 500 600 700 800 900 1000
2
2.2
2.4
Number of epochs
RMSE
Error Curves
720 725 730 735 740
5
10
15
20
Time in Hrs
Vwind in m/s
Wind-Speed and ANFIS Prediction for one day ahead
Actual
Predicted
720 725 730 735 740
-5
0
5
Time in Hrs
Error in m/s
Prediction Errors
0 2 4 6 8 10 12 14 16 18 20
0
2
4
6
8
10
12
14
16
18
20
Actual month data
Predicted month data
Scattered plot for actual vs predicted month data
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The output training data corresponds to the trajectory prediction.
Target(k) x(k P) (23)
Where k is the time instant in hours and P is the period to be predicted ( P = 24 in this case). The training
input/output data is a structure whose first component is the four-dimensional input Data(k) as in (22), and its
second component is the output Target(k) as in (23). There are 720 input/output data points. These data points
are used for the ANFIS training (these became the training data set), while only part of them are used as checking
data to validate the identified fuzzy model.
Figure 7.Upper graph is RMSE result for the model-I, Middel graph is actual and prediction of 24-Hrs ahead
,where the lower graph shows the prediction error
The one month data points (July 2013) were plotted earlier as the last graph of Fig. 2 to illustrate the data
used for the training. These data points are then rearranged as five vectors of shifted wind-speed recordings (each
vector is 24-Hrs shifted from its corresponding consequent vector). Training the ANFIS is done based on the
concept of time-series prediction. RMSE resulted from the training epochs of the ANN is shown in theupper
graph of Fig. 7. Data is then used to validate the ANFIS by predicting 24-Hrs ahead. The prediction of July 31st is
shown in middle graph of Fig. 7, while the lower graph illustrates the prediction error which shows a quite similar
prediction error for model-II as for model-I. The mean value of the error is found to be around 0.8545 m/s with
MAE of 1.1975 m/s. The results obtained by model-II is similar to the results of the previous model (model-I),
but it has a significant advantage over model-I. This advantage is that model-I has much more data points used in
the training step (model-II uses only 14% of model-I data points). Thus, model-II is preferred over model-I.
Figure 8 gives scattered plot for actual speed verses predicted speed month data of model II
Figure 8. Scattered plot for actual vs predicted month data
0 1000 2000 3000 4000 5000 6000 7000
1.5
2
Number of epochs
RMSE
Error Curve
720 725 730 735 740
0
10
Predicted Vwind m/s
Time in Hrs
Wind-Speed and ANFIS Prediction for one day ahead
Actu al
Predicted
720 725 730 735 740
-2
0
2
Predicted error m/s
Time in Hrs
Prediction Errors
0 2 4 6 8 10 12 14 16 18
0
2
4
6
8
10
12
14
16
18
Actual month data
Predicted month data
Scattered plot for actual vs predicted month data
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1.3. Model-III: 24-Hrs Ahead Based on one month Data with Kalman Filter
The statistical uncertainty is the randomness or error that comes from different sources; the five types
of uncertainty that emerge from the imprecise knowledge are:
Process uncertainty: dynamic randomness.
Modeling uncertainty: wrong specification of the model structure.
Measurement uncertainty: error on observed quantities.
Implementation uncertainty: consequence of the variability.
Estimate uncertainty: appear from any source of uncertainties or a combination of them, and it is called
inexactness and imprecision.
Wind speed measurements obtained from [12], are subjected to some sort of uncertainties which are
presented as vagueness of the wind speed value due to noise contents. KF is commonly used to filter out noisy
data as it is considered the best linear unbiased estimator (BLUE). In Model-II; data set is allowed to pass initially
through KF for the purpose of minimizing the error covariance exhibited by the data set as shown in Fig. 9.
Estimated (filtered) data are then rearranged to create a mapping from four samples wind-speed data points,
sampled every twenty four hours, to a predicted future of twenty four hours.
Figure 9. Block diagram of ANFIS with Kalman Filter (KF)
( ) ˆ( 3 ) ˆ( 2 ) ˆ( ) ˆ( ) Data E k x k p x k p x k p x k
(24)
Where:
( ) Data E k = Estimated data Vector
The output training data corresponds to the trajectory prediction.
Estimated Target k xˆ(k p) (25)
Where k is the time instant in hours and p is the period to be predicted ( p = 24 in this case). The training
input/output data is a structure whose first component is the four-dimensional input ( ) Data E k as in (24), and its
second component is the output Estimated Target (k) as in (25). There are 720 input/output data points. These
data points are used for the ANFIS training (these became the training data set), while only part of them are used
as checking data to validate the identified fuzzy model.
Figure 10.Upper graph is RMSE result for the model-I, Middel graph is actual and prediction of 24-Hrs ahead
,where the lower graph shows the prediction error
RMSE resulted from the training epochs of the ANN is shown in upper graph of Fig.10. Data is then
used to validate the ANFIS by predicting 24-Hrs ahead. The prediction of July 31st is shown in middle graph of
Fig. 10, while the lower graph illustrates the prediction error. The mean value of the error is found to be around
0 1000 2000 3000 4000 5000 6000 7000
0.6
0.8
1
Number of epochs
RMSE
Error Curves
720 725 730 735 740
0
5
10
15
Predicted Vwind m/s
Time in Hrs
Wind-Speed and ANFIS Prediction for one day ahead
Ac tual
Predicted
720 725 730 735 740
-2
0
2
Predicted error m/s
Time in Hrs
Prediction Errors
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0.0804 m/s with MAE of 0.6624 m/s. The results obtained by model-III arebetter than the results of the previous
model (model-II), because it has a significant advantage over model-II. This advantage is that model-III has low
mean absolute error this mean the prediction is more accurate by 55.5%. Thus, model-III is preferred over model-
II. Figure 11 gives scattered plot for actual speed verses predicted speed month data of model III
Figure 11. Scattered plot for actual vs predicted month data
Table I. shows accuracy study between the three proposed models including the used forecasting period
which is twenty four hours during seven months for the first model and one month for the next both of models.
The comparison discusses: RMSE, ME, MAE and Mean Absolute Percentage Error (MAPE).
Model-III using KF showed better accuracy based on error calculation. Thus; model-III is better than
model-I because MAPE has been improved by 51.45% as shows in equation (26):
MAPE of Model-III
Enhancement Ratio = (1- )
MAPE of Model-I
(26)
10.31
(1 ) 63.17%
28
And model-III is better than model-II because MAPE has been improved by 51.45% as shows in equation
(27):
MAPE of Model-III
Enhancement Ratio = (1- )
MAPE of Model-II
(27)
10.31
(1 ) 51.45%
21.24
Table I. Accuracy study for wind-speed forecasting.
Model-I Model-II Model-III
Forecasting Period 24-Hrs 24-Hrs 24-Hrs
Amount of data
points
5040 720 720
RMSE (m/s) 1.9782 1.3466 0.7652
Mean Error (m/s) 0.2645 0.8545 0.0804
MAE (m/s) 1.6319 1.1975 0.6624
MAPE (%) 28.00 21.24 10.31
V. Conclusion
In this paper, three effective time series stochastic wind models for Egypt’s north-western coast were
proposed and optimized using ANFIS and Kalman filter.
Model-I based on real wind-speed data sets for the month of July in the years 2007 through 2013; the
target was to predict wind-speed 24-Hrs ahead. Model-I accuracy (MAE)is 1.6319 m/s. Model-II and model-III
are both based on one month of data to predict 24-Hrs (July 2013);which have the advantage of using only 14%
of the data block size and improve the accuracy in the same time. In model-III an initial stage of kalman filter has
been added. KF stage has filtered out noise exhibited from measurement uncertainty. Model-III showed better
accuracy over model-I by approximately 63.17% mean absolute error and by approximately 51.45% mean
absolute error for model-II.
0 2 4 6 8 10 12 14
0
2
4
6
8
10
12
14
Actual month data
Predicted month data
Scattered plot for actual vs predicted month data
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