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This paper presents a method using an artificial neural network for creating an optimal model of energy consumption in wastewater treatment plant (WWTP) using activated sludge process. The advantage of this method is the use data usually measured in most of WWTP to optimize the energy consumption of the biological process. This method consists of selecting the data that represent the best energy consumption using different performance criteria then use this data to train a deep neural network. The procedure of selection is divided into two parts, in the first selection we selected the data that respect the environmental standards, and in the second part we selected the data with optimal energy consumption using different pollution indicators, and this data was used to train a deep neural network, finally the best model was used to estimate the energy savings on the data not selected. The model showed good results with a coefficient of determination that varies between 90% and 92% in training period and 74%–82% in testing period, the application of the best model on the data not selected showed a gain in energy for the most of the data. • High values of performance criteria in training and testing period • Determination of three classes of energy consumption: Underconsumption, overconsumption and optimal consumption. • Determination of warning limits of overconsumption of energy. [ABSTRACT FROM AUTHOR]