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The economic emission dispatch (EED) assumes a lot of significance to meet the clean energy requirements of the society, while at the same time minimising the cost of generation. The solution schemes in an attempt to arrive at the global best through the use of evolutionary algorithms are however inadequate to cater to problems of large size. The search based EED approaches are computationally inefficient particularly for problems with large number of Lecturer in Computer Science and Engineering,

This paper presents a dragonfly optimization (DFO) based ANN model for predicting India's primary fuel demand. It involves socio-economic indicators such as population and per capita GDP and uses two ANNs, which are trained through DFO algorithm. The method optimizes the connection weights of ANN models through effectively searching the problem space in finding the global best solution. Primary fuel demands during the years 1990-2012 forms the data for training and validating the model. The proposed model requires an input, the year of the forecast, and predicts the primary fuels' demand. The forecasts up to the year 2025 are compared with that of the RM with a view to illustrate the accuracy. General Termsforecasting, primary energy fuels Keywords artificial neural network, dragonfly optimization, regression model.