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Forecasting of Electricity Demand by Hybrid ANN-PSO Models

Forecasting of Electricity Demand by Hybrid ANN-PSO Models

Atul Anand, L. Suganthi
Copyright: © 2017 |Volume: 6 |Issue: 4 |Pages: 18
ISSN: 2160-9500|EISSN: 2160-9543|EISBN13: 9781522515241|DOI: 10.4018/IJEOE.2017100105
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MLA

Anand, Atul, and L. Suganthi. "Forecasting of Electricity Demand by Hybrid ANN-PSO Models." IJEOE vol.6, no.4 2017: pp.66-83. http://doi.org/10.4018/IJEOE.2017100105

APA

Anand, A. & Suganthi, L. (2017). Forecasting of Electricity Demand by Hybrid ANN-PSO Models. International Journal of Energy Optimization and Engineering (IJEOE), 6(4), 66-83. http://doi.org/10.4018/IJEOE.2017100105

Chicago

Anand, Atul, and L. Suganthi. "Forecasting of Electricity Demand by Hybrid ANN-PSO Models," International Journal of Energy Optimization and Engineering (IJEOE) 6, no.4: 66-83. http://doi.org/10.4018/IJEOE.2017100105

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Abstract

Developing economies need to invest in energy projects. Because the gestation period of the electric projects is high, it is of paramount importance to accurately forecast the energy requirements. In the present paper, the future energy demand of the state of Tamil Nadu in India, is forecasted using an artificial neural network (ANN) optimized by particle swarm optimization (PSO) and by General Algorithm (GA). Hybrid ANN Models have the potential to provide forecasts that perform well compared to the more traditional modelling approaches. The forecasted results obtained using the hybrid ANN-PSO models are compared with those of the ARIMA, hybrid ANN-GA, ANN-BP and linear models. Both PSO and GA have been developed in linear and quadratic forms and the hybrid ANN models have been applied to five-time series. Amongst all the hybrid ANN models, ANN-PSO models are the best fit models in all the time series based on RMSE and MAPE.