Electrical energy demand forecasting plays a pivotal role as a decision support tool in the modern power industry. The focus of the paper is to propose a hybrid approach for the selection of the most influential input variables for the training and testing of neural network based hybrid models. The combined influence of the genetic algorithm and correlation analysis are used in this technique. The significance of the selected input variable vectors is studied to analyze their effects on the prediction process. Another objective of the study is to develop and compare the prediction models for electrical energy demand of one day-ahead. These models are developed by integrating multilayer perceptron neural network and evolutionary optimization techniques. Genetic algorithm and simulated annealing techniques are used to optimize the control parameters of the neural network. The results show that the neural network optimized with genetic algorithm and trained with an optimally and intelligently selected input vector containing historical load and meteorological variables produced the best prediction accuracy. Keywords - artificial neural network; mean absolute percentage error; genetic algorithm; simulated annealing; correlation analysisAbstract � Electrical energy demand forecasting plays a pivotal role as a decision support tool in the modern power industry. The focus of the paper is to propose a hybrid approach for the selection of the most influential input variables for the training and testing of neural network based hybrid models. The combined influence of the genetic algorithm and correlation analysis are used in this technique. The significance of the selected input variable vectors is studied to analyze their effects on the prediction process. Another objective of the study is to develop and compare the prediction models for electrical energy demand of one day-ahead. These models are developed by integrating multilayer perceptron neural network and evolutionary optimization techniques. Genetic algorithm and simulated annealing techniques are used to optimize the control parameters of the neural network. The results show that the neural network optimized with genetic algorithm and trained with an optimally and intelligently selected input vector containing historical load and meteorological variables produced the best prediction accuracy. © 2017, UK Simulation Society. All rights reserved.