Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

Atul Anand and L Suganthi
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Atul Anand: Department of Management Studies, College of Engineering, Guindy, Anna University, Chennai, Tamil Nadu 600025, India
L Suganthi: Department of Management Studies, College of Engineering, Guindy, Anna University, Chennai, Tamil Nadu 600025, India

Energies, 2018, vol. 11, issue 4, 1-15

Abstract: 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: electricity demand; ANN; PSO; GA; hybrid optimization; forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (7)

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