Energy management systems can be improved by using artificial intelligence techniques such as neural networks and genetic algorithms for modelling and optimising equipment and system energy consumption. This paper proposes modelling ball mill consumption as used in the cement industry from field variables. The regression model was based on artificial neural networks for predicting the electricity consumption of the mill's main drive and evaluating established consumption rate performance. This research showed the influence of the amount of pozzolanic ash, gypsum and clinker on a mill's power consumption; the dose determined according to the model ensured minimum energy consumption using a simple genetic algorithm. The estimated savings potential from the proposed dose was 36 600 kWh/year for mill number 1, representing $5,793.78 / year and a 33,708 kg CO2 / year reduction in the environmental impact of gas left to escape.