According to the concept of Industry 4.0, the control of energy costs in a large transport corporation should be based on the use of artificial intelligence, the main focus of which is machine learning. It is also necessary to take into account the human factor - the unwanted activity of corporation employees who use information available only to them to achieve their own goals. The article examines the problem of reducing energy costs in a 3-tier transport corporation, which has regional offices, which are subordinate to local enterprises. The plant management knows own abilities of energy savings better than the regional management. Respectively, regional management knows these abilities better than the corporate governance. Therefore, both regional and plant management can manipulate energy expenditures to get more stimuli from the corporate governance. Such unwanted activity can result in excess energy consumption by the corporation. The goal is to minimize the energy costs of the corporation. To achieve this goal, a mechanism for organizational control of energy costs in a 3-tier transport corporation is proposed. This mechanism includes multilevel machine learning and stimulating procedures. Sufficient conditions are found for the synthesis of such a mechanism in which the existing possibilities of reducing energy costs in a transport corporation are used. The advantage of this mechanism over existing methods of learning classification in organizational systems (such as artificial neural networks or self-organizing maps) is to stimulate energy savings. The use of such a mechanism is illustrated by the example of 3-tier control system for energy consumption of JSC Russian Railways.