Artificial Neural Network Based Prediction Of Wind Turbine Power Curve Using Various Training Algorithms

2021 ◽  
Author(s):  
Muhammad U Saram ◽  
Jianming Yang ◽  
Zaheer Ahmad ◽  
Sadaf Zahoor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Turbine ◽  
Training Algorithms ◽  
Power Curve ◽  
Artificial Neural

Wind turbine power curve modelling using artificial neural network

2016 ◽  
Vol 89 ◽  
pp. 207-214 ◽  
Author(s):  
Francis Pelletier ◽  
Christian Masson ◽  
Antoine Tahan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Turbine ◽  
Power Curve ◽  
Artificial Neural

Wind turbine power modelling and optimization using artificial neural network with wind field experimental data

2020 ◽  
Vol 280 ◽  
pp. 115880 ◽  
Author(s):  
Haiying Sun ◽  
Changyu Qiu ◽  
Lin Lu ◽  
Xiaoxia Gao ◽  
Jian Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Wind Turbine ◽  
Wind Field ◽  
Artificial Neural ◽  
Modelling And Optimization

scholarly journals Application of artificial neural network to predict properties of diesel-biodiesel blends

1970 ◽  
Vol 6 (2) ◽  
pp. 98-103 ◽  
Author(s):  
Jatinder Kumar ◽  
Ajay Bansal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Least Squares ◽  
Statistical Technique ◽  
Training Algorithms ◽  
Biodiesel Blends ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The experimental determination of various properties of diesel-biodiesel mixtures is very time consuming as well as tedious process. Any tool helpful in estimation of these properties without experimentation can be of immense utility. In present work, other tools of determination of properties of diesel-biodiesel blends were tried. A traditional statistical technique of linear regression (principle of least squares) was used to estimate the flash point, fire point, density and viscosity of diesel and biodiesel mixtures. A set of seven neural network architectures, three training algorithms along with ten different sets of weight and biases were examined to choose best Artificial Neural Network (ANN) to predict the above-mentioned properties of dieselbiodiesel mixtures. The performance of both of the traditional linear regression and ANN techniques were then compared to check their validity to predict the properties of various mixtures of diesel and biodiesel. Key words: Biodiesel; Artificial Neural Network; Principle of least squares; Diesel; Linear Regression. DOI: 10.3126/kuset.v6i2.4017Kathmandu University Journal of Science, Engineering and Technology Vol.6. No II, November, 2010, pp.98-103

scholarly journals Fast Linear Adaptive Skipping Training Algorithm for Training Artificial Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
R. Manjula Devi ◽  
S. Kuppuswami ◽  
R. C. Suganthe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training Model ◽  
Training Data ◽  
Experimental Result ◽  
Training Algorithms ◽  
Data Set ◽  
Training Time ◽  
Input Sample ◽  
Artificial Neural

Artificial neural network has been extensively consumed training model for solving pattern recognition tasks. However, training a very huge training data set using complex neural network necessitates excessively high training time. In this correspondence, a new fast Linear Adaptive Skipping Training (LAST) algorithm for training artificial neural network (ANN) is instituted. The core essence of this paper is to ameliorate the training speed of ANN by exhibiting only the input samples that do not categorize perfectly in the previous epoch which dynamically reducing the number of input samples exhibited to the network at every single epoch without affecting the network’s accuracy. Thus decreasing the size of the training set can reduce the training time, thereby ameliorating the training speed. This LAST algorithm also determines how many epochs the particular input sample has to skip depending upon the successful classification of that input sample. This LAST algorithm can be incorporated into any supervised training algorithms. Experimental result shows that the training speed attained by LAST algorithm is preferably higher than that of other conventional training algorithms.

Effective Implementation of Value Engineering Using Artificial Neural Network Aid of Optimization Techniques

2019 ◽  
Vol 27 (03) ◽  
pp. 1950022 ◽  
Author(s):  
M. Prem Swarup ◽  
A. Prabhu Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Research Work ◽  
Optimization Techniques ◽  
Value Engineering ◽  
Training Algorithms ◽  
Air Conditioner ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Value Engineering (VE) is a method for characterizing the developed requirements of a product, and it is concerned with the selection of less excessive conditions. VE can understand and improve the optimal outcome such as quantity, security, unwavering quality and convertibility of each managerial unit. It is an incredible solving tool that can diminish costs while preserving or improving performance and quality requirements. In this research work, VE is presented to calculate the heating cost and cooling cost of the air conditioner with the assistance of an Artificial Neural Network (ANN) with an optimization model. This ANN model effectively chooses the maximum number of sources obtainable and the source respective method with low functional cost and energy consumption. For improving the prediction accuracy of VE in the ANN model, we have incorporated some training algorithms and optimized the network hidden layer and hidden neuron by Opposition Genetic Algorithm (OGA). From the results, trained ANN with OGA predicts the output with 96.02% accuracy and also minimum errors compared with the existing GA process.

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