Ali Azadeh

TL;DR: In this paper, an Artificial Neural Network (ANN) approach was used to forecast long-term electricity consumption in high energy consumption industrial sectors in Iran from 1979 to 2003, and the ANN forecast is compared with actual data and the conventional regression model through ANOVA.

TL;DR: It is shown that neural networks dominate time series approach form the point of yielding less mean absolute percentage error (MAPE) error and utilization of ANN instead of time series to obtain better predictions for energy consumption.

TL;DR: In this article, an integrated algorithm for forecasting monthly electrical energy consumption based on genetic algorithm (GA), computer simulation and design of experiments using stochastic procedures is presented. And the proposed algorithm may identify conventional time series as the best model for future electricity consumption forecasting because of its dynamic structure.

TL;DR: In this paper, the authors presented an integrated algorithm for forecasting monthly electrical energy consumption based on artificial neural network (ANN), computer simulation and design of experiments using stochastic procedures.

TL;DR: This paper illustrates an Artificial Neural Network approach based on supervised multi layer perceptron (MLP) network for the electrical consumption forecasting and shows the advantage of ANN methodology through analysis of variance (ANOVA).