Influence of Machine Learning Method Choice on the Accuracy of Power Load Forecast Models and HPP Cascade Mode

This article proposes a new approach to operational forecasting of power load schedules with the use of machine learning methods. In order to increase the accuracy of models and algorithms for operational forecasting of power load schedules, testing of several machine learning methods was implemented. The developed models were tested in the Python programming language using the sktime and scikit-learn libraries. A calculation of power modes was carried out, which showed high comparability of the results with the actual values of the mode. The residual values of the controlled parameters based on the results of using the calculated and predicted value of the power load with the use of machine learning methods showed a higher comparability of the results with the real power mode. This allows us to talk about obtaining a more accurate result when using the forecast of the power load in nodes with the use of machine learning methods and their possible use in dispatch centers as a basis for planning long-term conditions and coordinating repair schedules for the power grid equipment.