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• W2080370499 abstract "This study presents application of particle swarm optimization (PSO) and genetic algorithm (GA) methods to estimate electricity demand in Iran's industrial sectors, based on economic indicators. The economic indicators used in this study are number of customers, gross domestic product (GDP), electricity production and price. The models developed in two forms (exponential and linear). Electricity consumption in Iran's industrial sector from 1981 to 2005 is considered as the case of this study. The available data is partly used for finding the optimal, or near optimal, values of the weighting parameters (1981–1999) and partly for testing the models (2000–2005). For the best results of GA, the relative error averages were 1.13% and 1.29% for GA − DE M exponential and GA − DE M linear and for PSO were 1.03% and 1.69% for PSO − DE M exponential and PSO − DEMlinear. Electricity consumption is forecasted up to year 2030." @default.
• W2080370499 created "2016-06-24" @default.
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• W2080370499 date "2011-04-01" @default.
• W2080370499 modified "2023-10-06" @default.
• W2080370499 title "ASSESSMENT OF ELECTRICITY DEMAND IN IRAN'S INDUSTRIAL SECTOR USING DIFFERENT INTELLIGENT OPTIMIZATION TECHNIQUES" @default.
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• W2080370499 doi "https://doi.org/10.1080/08839514.2011.559572" @default.
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