Development of a Question and Answer for the Subject Area "Management of a Metallurgical Plant"

In this article, based on multiple tests, concerning the subject area “Management of a metallurgical plant”, the precision of the requests by users and the results given in the chatbots proved that there were some errors. The results showed that the system could not always give the accurate results.I would like to thank my supervisor Mr. Aksyonov K. A. for guiding me through my research

Forecasting energy characteristics of photoelectric stations by the methods of learning solution trees

В статье представлены результаты прогнозирования выработки электроэнергии фотоэлектрическими (солнечными) электростанциями (ФЭС) методами обучения деревьев решений. Для прогнозирования были использованы деревянные модели, основаны на деревьях решений DecisionTree, GradientBoosting, RandomForest. Для оценки точности прогнозирования оценивались среднеквадратичная ошибка (MSE), средняя абсолютная ошибка (MAE), коэффициент детерминации (R2). At present, the results of forecasting the generation of electricity by photovoltaic (solar) power plants by methods of training decision trees. For forecasting, wooden models was used, based on decision trees DecisionTree, GradientBoosting, RandomForest. To assess the prediction accuracy, the mean square error (MSE), the mean absolute error (MAE), and the coefficient of determination (R2) was estimated