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Anbazhagan, S.
- Athens Seasonal Variation of Ground Resistance Prediction Using Neural Networks
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Authors
Affiliations
1 Department of Electrical Engineering, Annamalai University, IN
1 Department of Electrical Engineering, Annamalai University, IN
Source
ICTACT Journal on Soft Computing, Vol 6, No 1 (2015), Pagination: 1113-1116Abstract
The objective in ground resistance is to attain the most minimal ground safety esteem conceivable that bodes well monetarily and physically. An application of artificial neural networks (ANN) to presage and relegation has been growing rapidly due to sundry unique characteristics of ANN models. A decent forecast is able to capture the dubiousness associated with those ground resistance. A portion of the key instabilities are soil composition, moisture content, temperature, ground electrodes and spacing of the electrodes. Propelled by this need, this paper endeavors to develop a generalized regression neural network (GRNN) to predict the ground resistance. The GRNN has a single design parameter and expeditious learning and efficacious modeling for nonlinear time series. The precision of the forecast is applied to the Athens seasonal variation of ground resistance that shows the efficacy of the proposed approach.Keywords
Ground Resistance, Generalized Regression Neural Network, Forecasting.
References
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- S. Anbazhagan and N. Kumarappan, “Day-ahead Deregulated Electricity Market Price Forecasting using Recurrent Neural Network”, IEEE Systems Journal, Vol. 7, No. 4, pp. 866-872, 2013.
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- Binary Classification of Day-Ahead Deregulated Electricity Market Prices Using Neural Network Input Featured by DCT
Abstract Views :144 |
PDF Views:0
Authors
Affiliations
1 Department of Electrical Engineering, Annamalai University, IN
1 Department of Electrical Engineering, Annamalai University, IN
Source
ICTACT Journal on Soft Computing, Vol 2, No 4 (2012), Pagination: 384-390Abstract
There is a general consensus that the movement of electricity price is crucial for electricity market. The binary electricity price classification method is as an alternative to numerical electricity price forecasting due to high forecasting errors in various approaches. This paper proposes a binary classification of day-ahead electricity prices that could be realized using discrete cosine transforms (DCT) based neural network (NN) approach (DCT-NN). These electricity price classifications are important because all market participants do not to know the exact value of future prices in their decision-making process. In this paper, classifications of electricity market prices with respect to pre-specified electricity price threshold are used. In this proposed approach, all time series (historical price series) are transformed from time domain to frequency domain using DCT. These discriminative spectral co-efficient forms the set of input features and are classified using NN. The binary classification NN and the proposed DCT-NN were developed and compared to check the performance. The simulation results show that the proposed method provides a better and efficient method for day-ahead deregulated electricity market of mainland Spain.Keywords
Price Forecasting, Discrete Cosine Transforms, Neural Network, Binary Electricity Price Classification, Electricity Market.- An Elm for Bi-Classification of Vertically Bundled Electricity Market Prices
Abstract Views :178 |
PDF Views:3
Authors
Affiliations
1 Department of Electrical Engineering, Annamalai University, IN
2 Anubavam Technologies Private Limited, US
1 Department of Electrical Engineering, Annamalai University, IN
2 Anubavam Technologies Private Limited, US
Source
ICTACT Journal on Soft Computing, Vol 8, No 1 (2017), Pagination: 1567-1567Abstract
Electricity price forecasting is a challenging problem owing to the very great volatility of price which depends on many factors. This is especially prominent for both producers and consumers where a versatile price forecasting is crucial. This paper contributes an extreme learning machine (ELM) to classify the prices. These price classifications are essential since all market players do not know the precise value of future prices in their deciding procedure. In this paper, bi-classification model is proposed for prices utilizing the pre-specified price threshold. Three alternative classification models based on neural networks (NNs) are also proposed in bi-classification of prices. The performance of the proposed models is compared in terms of classification error and accuracy. The simulation results show that the ELM classification model is superior compared to three other classification models based on NNs. The performances of our models are evaluated using real data from vertically unbundled mainland Spain power system market.Keywords
Electricity Price Classification, Extreme Learning Machines (ELM), Power System Market, Price Forecasting.References
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- S. Anbazhagan and N. Kumarappan, “A Neural Network Approach to Day-Ahead Deregulated Electricity Market Prices Classification”, Electric Power Systems Research, Vol. 86, pp. 140-150, 2012.
- S. Anbazhagan and N. Kumarappan, “Day-Ahead Deregulated Electricity Market Price Classification using Neural Network Input Featured by DCT”, International Journal of Electrical Power and Energy Systems, Vol. 37, No. 1, pp. 103-109, 2012.
- S. Anbazhagan and N. Kumarappan, “Binary Classification of Day-Ahead Deregulated Electricity Market Prices using Neural Network Input Featured by DCT”, ICTACT Journal on Soft Computing, Vol. 2, No. 4, pp. 384-390, 2012.
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- Artificial Neural Networks to Detect Facial Abnormalities through Cephalometric Radiography using Bjork Analysis
Abstract Views :242 |
PDF Views:2
Authors
Affiliations
1 Department of Electrical Engineering, Faculty of Engineering and Technology, Annamalai University, IN
1 Department of Electrical Engineering, Faculty of Engineering and Technology, Annamalai University, IN
Source
ICTACT Journal on Soft Computing, Vol 11, No 4 (2021), Pagination: 2391-2401Abstract
The dental and skeletal relationships in the head are studied in Cephalometric analysis. This research work addresses Bjork’s analysis for the classification of patients. In this research work, the backpropagation neural network (BPNN), and generalized regression neural network (GRNN) classifiers are used and studied for the diagnosis of Cephalometric analysis. In this study, a total of 304 (male 109, female 195) patient’s case records were collected for this study. All the collected clinical data are used for classification. For training and testing the proposed models, patients' data were separated by four-fold cross-validation. Based on Bjork analysis, experimental results show that GRNN provided achieving the performance of 97.39% of good classification results when compared to the BPNN model. The GRNN approach is feasible and was found to be achieving a performance of 97.39% of the correct detection of patients.Keywords
Bjork’s Analysis, Cephalometric Analysis, Back Propagation Neural Network, Generalized Regression Neural Network.References
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