In order to obtain high-quality predictive results of electricity consumption in the context of different countries and years, theoretical foundations and terminology regarding the use of "Decision Tree" models and their ensemble architecture "Random Forest" were considered. This architecture helped to find the optimal forecast result without such unpleasant effects as: overtraining, model insufficiency. MAE and MSE metrics were considered and implemented to determine the quality, such a set can show business value, for example, MAE will only show the absolute error, which can tell the quality of the model for decision makers, and MSE metric, which can be useful for neural network model engineers for quality improvement using gradient descent. To implement the forecast model, the Python programming language was used using Numpy, Pandas and Sklearn libraries. The result of the theoretical study of the predictive model is a consistent study of details and definitions in relation to the theoretical basis for understanding what problems are solved by decision trees and why they can be used to create a forecast in the energy field. The result of practical implementation is a model with an absolute average error of 6.90%, which means that the model is adequate and workable, it can be used both as a basis for forecasting and as a self-sufficient model. The study provides an algorithm and demonstrates the implementation of a sequence of actions for creating a predictive model regardless of its type and architecture, providing insight not only in the details of implementation with the help of specific tools, but also at a more abstract level of description of actions. Also demonstrated is work with data processing to meet the needs of models, creation of new variables, and data transformation, which is also a mandatory practice for obtaining quality results. The absolute average error gives general information about the quality of the created model, but specific results can also give certain information in terms of a specific country, for example, the result of the forecast for Ukraine for 2021 is -1.90 value of the target variable "Net electricity import as share of demand", in while the true value is -3.40, the difference between the two figures is even smaller than the expected error.