Mathematical Modeling and Planning of Energy Production using a Neural Network

This paper examines the investigation and optimization of existing approaches for the efficient deployment of renewable energy-based power generation facilities and a genetic algorithm for predicting the operating mode with the help of efficient deployment of production facilities. The developed genetic algorithm model is based on the use of a radial basic neural network. As a result of these neural networks, it becomes possible to minimize the cost of data processing time and use them in solving technical and economic problems that require high-speed processing. The proposed approach allows for obtaining the most accurate and justified option for the deployment of renewable energy sources to solve the problem of active power reserves and allows for forecasting with an error of no more than 20%.