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Forecasting electricity consumption is currently one of the most important scientific and practical tasks in the field of electric power industry. The early retrieval of data on expected load profiles makes it possible to choose the optimal operating mode of the system. The resultant forecast accuracy significantly affects the performance of the entire electrical complex and the operating conditions of the electricity market. This can be achieved through using a model of total electricity consumption designed with an acceptable margin of error. This paper proposes a new method for predicting power consumption in all nodes of the power system through the determination of rank coefficients calculated directly for the corresponding voltage level, including node substations, power supply zones, and other parts of the power system. The forecast of the daily load schedule and the construction of a power consumption model was based on the example of nodes in the central power system in Mongolia. An ensemble of decision trees was applied to construct a daily load schedule and rank coefficients were used to simulate consumption in the nodes. Initial data were obtained from daily load schedules, meteorological factors, and calendar features of the central power system, which accounts for the majority of energy consumption and generation in Mongolia. The study period was 2019–2021. The daily load schedules of the power system were constructed using machine learning with a probability of 1.25%. The proposed rank analysis for power system zones increases the forecasting accuracy for each zone and can improve the quality of management and create more favorable conditions for the development of distributed generation.

Solar energy is an unlimited and sustainable energy source that holds great importance during the global shift towards environmentally friendly energy production. However, integrating solar power into electrical grids is challenging due to significant fluctuations in its generation. This research aims to develop a model for predicting solar radiation levels using a hybrid power system in the Gorno-Badakhshan Autonomous Oblast of Tajikistan. This study determined the optimal hyperparameters of a multilayer perceptron neural network to enhance the accuracy of solar radiation forecasting. These hyperparameters included the number of neurons, learning algorithm, learning rate, and activation functions. Since there are numerous combinations of hyperparameters, the neural network training process needed to be repeated multiple times. Therefore, a control algorithm of the learning process was proposed to identify stagnation or the emergence of erroneous correlations during model training. The results reveal that different seasons require different hyperparameter values, emphasizing the need for the meticulous tuning of machine learning models and the creation of multiple models for varying conditions. The absolute percentage error of the achieved mean for one-hour-ahead forecasting ranges from 0.6% to 1.7%, indicating a high accuracy compared to the current state-of-the-art practices in this field. The error for one-day-ahead forecasting is between 2.6% and 7.2%.