Version 1
: Received: 28 November 2017 / Approved: 29 November 2017 / Online: 29 November 2017 (12:39:09 CET)
Version 2
: Received: 15 January 2018 / Approved: 16 January 2018 / Online: 16 January 2018 (07:44:04 CET)
How to cite:
Anand, A.; Suganthi, L. Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand. Preprints2017, 2017110190. https://doi.org/10.20944/preprints201711.0190.v1
Anand, A.; Suganthi, L. Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand. Preprints 2017, 2017110190. https://doi.org/10.20944/preprints201711.0190.v1
Anand, A.; Suganthi, L. Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand. Preprints2017, 2017110190. https://doi.org/10.20944/preprints201711.0190.v1
APA Style
Anand, A., & Suganthi, L. (2017). Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand. Preprints. https://doi.org/10.20944/preprints201711.0190.v1
Chicago/Turabian Style
Anand, A. and L Suganthi. 2017 "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand" Preprints. https://doi.org/10.20944/preprints201711.0190.v1
Abstract
In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP), per capita demand, income and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015 , the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the "as-it-is" scenario , the second scenario is based on milestones set for achieving goals of "Vision 2023" document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that the direct causality exists between GSDP and the electricity demand of the state.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Comment 1
Received:
30 November 2017
The commenter has declared there is no conflict of interests.
Comment:
The article integrates genetic algorithm and particle swarm optimization for the artificial neural network which gives better forecasting results. This research gives researchers a better way to think a step ahead in the area of forecasting the electricity and energy demand.
Received:
3 December 2017
Commenter:
Navneet garg
The commenter has declared there is no conflict of interests.
Comment:
Article is futuristic and gives defibining arguments for policy work for the government..accurate forecasting is the need for the govt to budget their future capex and optimising current op-ex..
The commenter has declared there is no conflict of interests.
The commenter has declared there is no conflict of interests.
Commenter: Navneet garg
The commenter has declared there is no conflict of interests.
The commenter has declared there is no conflict of interests.