The goal of this research is to develop appropriate machine learning models for predicting voltage values on the 400 kV side of the grid. The data used for training the models include two years of historical voltage data, along with hydrometeorological variables, with temperature being the most significant factor, supported by meteorological yearbooks. Additional input data include air humidity, wind direction, wind speed, precipitation, and the appearance of ice on power lines. The prediction of voltage values aims to forecast the engagement of static reactive reserves, with a sampling period of 10 minutes (min/max/avg). This data enables annual planning of energy imports and maintenance periods, as well as daily planning of capacity engagement. The next step in the research is the forecast of dynamic reactive reserves, where samples with a frequency of one second and more frequent samples would be used for model training.