## 7 Conclusions and Future Work  
In this article, we have described in sufficient detail the algorithm of using the DEA method for district heating enterprises. Therefore, readers can easily use the results of these experiments and repeat such calculations independently. Our experiments have demonstrated how much the performance indicators of the CHPP can change. In accordance with these indicators, the settings of input and output indicators for the sample are also changed to achieve efficiency. For example, for one experiment, the same CHPP can achieve maximum efficiency in operation, and for another it can be ineffective. When the goal to be achieved changes the conditions for the DEA task change. The results of calculations of the effectiveness of DEA models for the same sample may change dramatically. It can be concluded that for the possibility of application, as well as for the correctness of the application of the DEA method, an understanding of the application of this method to various models in a particular field is required. As well as understanding how and when the goal changes, it is necessary to understand that the indicators of inputs and outputs, the model itself, and the orientation of the DEA model change. The district heating system is a rather complex system with a large number of technical indicators. Therefore, achieving the efficiency of such systems is a complex and time-consuming process. The introduction of new methods for evaluating the effectiveness of such systems will solve a number of problems. In the future, it will lead to automation of management processes, due to the possibility of creating a DSS to increase efficiency based on the DEA method. It will also lead to an increase in the adaptability of the system to external factors due to the possibility of changing the settings of the DEA method when changing the goals of the DM. The data obtained from the experiments made it possible to observe changes in performance indicators when changing the goal of DM in the same sample. It was clearly demonstrated that the use of various DEA models and their orientations leads to changes in the settings of input and output indicators. Thus, only with a full understanding of the patterns and performance indicators of the studied field is the correct use of DEA models possible. The objectives of the DM may change in the course of the functioning of the enterprises due to rapidly changing external factors in the operating environment. Therefore, in these conditions, it is important to change the settings for building DEA models for specific enterprises, as well as their input and output parameters. The methodology of using DEA models for evaluating efficiency presented in this article makes it possible to apply the DEA method to improve the efficiency of fuel and energy companies, a context in which this method and methodology have not previously been used. In the article, we have shown how, when the goal of the decision maker changes, the performance indicator can change. This is a very important conclusion for understanding the change in efficiency when the operating conditions of the system change. Now, it has become clear how we could adjust the effectiveness when changing the goal of the decision maker. The results obtained in the experimental part will be the basis for programming an automated decision-making system in the future. There are 2 aspects on which we can continue working:  
1. To develop an automated decision support system for fuel and energy complex enterprises based on the algorithm and experimental results. 2. When using the DEA method, there are several usage restrictions. Further work can be continued on the development of a new efficiency method that can minimize the limitations of its use. The algorithm presented, the data obtained and the conclusions from the presented experiments will help in using the DEA method to increase the efficiency of fuel and energy companies.