In the promotion of modern real estates with a high level of energy consumption are the most important assessment of their energy efficiency. Increasing the level of technology in commercial buildings with digital infrastructure of accounting, control and management energy consumption has led to increased availability of data produced by the digital sensors. All this opens up huge opportunities for using of advanced mathematical models and machine learning methods that would improve the accuracy of forecasts of electricity consumption by commercial buildings, and thus improve estimates of energy saving. One of the most powerful algorithms in machine learning is gradient boosting (GBM). In this paper on GBM basis a method of the energy consumption profile modeling is proposed both for a separate building and for business centers. To evaluate its effectiveness advanced computer experiments were performed on real data of the energy consumption of commercial buildings. For this purpose different periods of model training were used, and its prediction accuracy was analyzed by several criteria. The results showed that the use of our model improved the accuracy forecasts of energy savings in more than 80 percent of cases compared to regression and random forest models. Copyright © 2018 for the individual papers by the papers' authors.