Energy efficiency in industry provides some promising solutions for industrial decarbonization and reduction of negative environ-mental impacts. Nowadays, the digitalization of the industry offers an intelligent industrial work network, which allows the use of learning algorithms for the prediction of energy consumption in order to lower the energy bill. This paper investigates different approaches used to predict energy consumption in industry, including Multiple Linear Regression (MLR), Decision Tree (DT), Artificial Neural Networks (ANN) and Recurrent Neural Networks (RNN) based on data collected of meteorological conditions, energy consumption, and lighting in the industry. The review results indicate that the MLR approach is the best forecasting method.