LAPSE:2020.0687
Published Article
LAPSE:2020.0687
Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand
Atul Anand, L Suganthi
June 23, 2020
In the present study Artificial Neural Network (ANN) has been optimized using a hybrid algorithm of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The hybrid GA-PSO algorithm has been used to improve the estimation of electricity demand of the state of Tamil Nadu in India. The ANN-GA-PSO model uses gross domestic product (GSDP); electricity consumption per capita; income growth rate and consumer price index (CPI) as predictors that affect the electricity demand. Using the historical demand data of 25 years from 1991 till 2015 it is found that ANN-GA-PSO models have higher accuracy and performance reliability than single optimization models such as ANN-PSO or ANN-GA. In addition, the paper also forecasts the electricity demand of the state based on “as-it-is” scenario and the scenario based on milestones set by the “Vision-2023” document of the state.
Keywords
ANN, electricity demand, forecasting, GA, hybrid optimization, PSO
Suggested Citation
Anand A, Suganthi L. Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand. (2020). LAPSE:2020.0687
Author Affiliations
Anand A: Department of Management Studies, College of Engineering, Guindy, Anna University, Chennai, Tamil Nadu 600025, India [ORCID]
Suganthi L: Department of Management Studies, College of Engineering, Guindy, Anna University, Chennai, Tamil Nadu 600025, India
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Journal Name
Energies
Volume
11
Issue
4
Article Number
E728
Year
2018
Publication Date
2018-03-23
Published Version
ISSN
1996-1073
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Original Submission
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PII: en11040728, Publication Type: Journal Article
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LAPSE:2020.0687
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doi:10.3390/en11040728
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Jun 23, 2020
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Calvin Tsay
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