Long Term Energy Demand Forecasting based on Hybrid, Optimization: Comparative Study

The objective of this research is to develop a long term energy demand forecasting model that used hybrid optimization. To accomplish this goal, a hybrid algorithm that combined a genetic algorithm and a local search algorithm method has been developed to overcome premature convergence. Model performances of hybrid algorithm were compared with former single algorithm model in estimating parameter values of an objective function to measure the goodness-of-fit between the observed data and simulated results. Averages error between two models was adopt to select the proper model for future projection of energy demand.