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Thangaraj, C.
- Prediction of India's Electricity Demand Using Anfis
Abstract Views :161 |
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Authors
Affiliations
1 Department of Electrical and Electronics Engineering, Kalasalingam University, IN
2 Department of Electrical and Electronics Engineering, Ramco Institute of Technology, IN
3 Vignan University, IN
1 Department of Electrical and Electronics Engineering, Kalasalingam University, IN
2 Department of Electrical and Electronics Engineering, Ramco Institute of Technology, IN
3 Vignan University, IN
Source
ICTACT Journal on Soft Computing, Vol 5, No 3 (2015), Pagination: 985-990Abstract
This study aims to provide an accurate and realistic prediction model for electricity demand using population, imports, exports, per capita Gross Domestic Product (GDP) and per capita Gross National Income (GNI) data for India. Four different models were used for different combinations of the above five input variables and the effect of input variables on the estimation of electricity demand has been demonstrated. In order to train the network 29 years data and to test the network 9 years data have been used. The future electricity demand for a period of 8 years from 2013 to 2020 has been predicted. The performance of the ANFIS technique is proved to be better than Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN).Keywords
ANFIS, ANN, Exports, GDP, GNI, Imports, Load Forecasting, MLR.- Application of Restart Covariance Matrix Adaptation Evolution Strategy (RCMA_-ES) to Generation Expansion Planning Problem
Abstract Views :190 |
PDF Views:0
Authors
Affiliations
1 Department of Electrical and Electronics Engineering, Kalasalingam University, IN
2 Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, IN
3 Anna University of Technology, Chennai, IN
1 Department of Electrical and Electronics Engineering, Kalasalingam University, IN
2 Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, IN
3 Anna University of Technology, Chennai, IN
Source
ICTACT Journal on Soft Computing, Vol 3, No 1 (2012), Pagination: 401-407Abstract
This paper describes the application of an evolutionary algorithm, Restart Covariance Matrix Adaptation Evolution Strategy (RCMAES) to the Generation Expansion Planning (GEP) problem. RCMAES is a class of continuous Evolutionary Algorithm (EA) derived from the concept of self-adaptation in evolution strategies, which adapts the covariance matrix of a multivariate normal search distribution. The original GEP problem is modified by incorporating Virtual Mapping Procedure (VMP). The GEP problem of a synthetic test systems for 6- year, 14-year and 24-year planning horizons having five types of candidate units is considered. Two different constraint-handling methods are incorporated and impact of each method has been compared. In addition, comparison and validation has also made with dynamic programming method.Keywords
Constraint Handling, Dynamic Programming, Generation Expansion Planning, Restart Covariance Matrix Adaptation Evolution Strategy and Virtual Mapping Procedure.- India's Electricity Demand forecast Using Regression Analysis and Artificial Neural Networks Based on Principal Components
Abstract Views :156 |
PDF Views:0
Authors
Affiliations
1 Department of Electrical and Electronics Engineering, Kalasalingam University, IN
2 Anna University of Technology, Chennai, IN
1 Department of Electrical and Electronics Engineering, Kalasalingam University, IN
2 Anna University of Technology, Chennai, IN