scholarly journals Electricity price forecasting in Turkey with artificial neural network models

2016 ◽  
Vol 13 (3) ◽  
pp. 150-158 ◽  
Author(s):  
Fazil Gökgöz ◽  
Fahrettin Filiz
Keyword(s):  
Neural Network ◽  
Electricity Market ◽  
Gradient Descent ◽  
Network Models ◽  
Electricity Price ◽  
Price Forecasting ◽  
Neural Network Models ◽  
Electricity Price Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Models

The electricity market has experienced significant changes towards deregulation with the aim of improving economic efficiency. The electricity pricing is a major consideration for consumers and generation companies in deregulated electric markets, so that offering the right price for electricity has become more important. Various methods and ideas have been tried for electricity price forecasting. Artificial neural networks have received much attention with its nonlinear property and many papers have reported successful experiments with them. This paper introduces artificial neural network models for day-ahead electricity market in Turkey. Using gradient descent, gradient descent with momentum, Broydan, Fletcher, Goldfarb and Shanno (BFGS) and Levenberg-Marquardt algorithm with different number of neuron and transfer functions, 400 different models are created. Performances of different models are compared according to their Mean Absolute Percentage (MAPE) values; the most successful models MAPE value is observed as 9.76%. Keywords: electricity price forecasting, neural networks, day-ahead electricity market, Turkey. JEL Classification: C02, C13, C45, C53

Electricity Market Forecasting using Artificial Neural Network Models Optimized by Grid Computing

2009 ◽  
Vol 42 (9) ◽  
pp. 273-277 ◽  
Author(s):  
Aishah Mohd Isa ◽  
Takahide Niimura ◽  
Noriaki Sakamoto ◽  
Kazuhiro Ozawa ◽  
Ryuichi Yokoyama
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Grid Computing ◽  
Electricity Market ◽  
Network Models ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Artificial Neural Network Models

Automatic Electrocardiographic Analysis Using Artificial Neural Network Models

2011 ◽  
Vol 403-408 ◽  
pp. 3587-3593
Author(s):  
T.V.K. Hanumantha Rao ◽  
Saurabh Mishra ◽  
Sudhir Kumar Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Models ◽  
Neural Network Models ◽  
Radial Basis ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
Self Organizing

In this paper, the artificial neural network method was used for Electrocardiogram (ECG) pattern analysis. The analysis of the ECG can benefit from the wide availability of computing technology as far as features and performances as well. This paper presents some results achieved by carrying out the classification tasks by integrating the most common features of ECG analysis. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, long term atrial fibrillation, sudden cardiac death and congestive heart failure. The R-R interval features were performed as the characteristic representation of the original ECG signals to be fed into the neural network models. Two types of artificial neural network models, SOM (Self- Organizing maps) and RBF (Radial Basis Function) networks were separately trained and tested for ECG pattern recognition and experimental results of the different models have been compared. The trade-off between the time consuming training of artificial neural networks and their performance is also explored. The Radial Basis Function network exhibited the best performance and reached an overall accuracy of 93% and the Kohonen Self- Organizing map network reached an overall accuracy of 87.5%.

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