Open AccessIncreasing the accuracy of forecasting the electricity consumption of an industrial enterprise by machine learning methods using the selection of significant features from a time series
Author(s) -
Nikita N. Sergeev,
Pavel Matrenin
Publication year - 2022
Publication title -
ipolytech journal
Language(s) - English
Resource type - Journals
eISSN - 2782-6341
pISSN - 2782-4004
DOI - 10.21285/1814-3520-2022-3-487-498
Subject(s) - artificial neural network , gradient boosting , decision tree , computer science , machine learning , artificial intelligence , adaboost , boosting (machine learning) , ensemble learning , feature selection , random forest , autoregressive integrated moving average , support vector machine , schedule , time series , operating system