title: ÐÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ñ ÑÐ½ÐµÑÐ³ÐµÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÐµÐºÑÐ°
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Using machine learning methods to predict the state of an energy object
Creators: name: ÐÑÑÐ°Ð²Ð»ÐµÐ²Ð°, Ð­Ð²ÐµÐ»Ð¸Ð½Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: Ð¿ÑÐµÐ´Ð¸ÐºÑÐ¸Ð²Ð½Ð°Ñ Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÐºÐ° ÑÐ½ÐµÑÐ³ÐµÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð²
ÑÐµÑ Ð½Ð¸ÑÐµÑÐºÐ¾Ðµ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ðµ
Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
ÑÐ·ÑÐº Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Python
Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐºÐ¸ Pandas
Matplotlib
Numpy
Scikit-Learn
predictive analytics of energy objects
artificial neural network
Python programming language
libraries Pandas

description: ÐÐ¾ÐºÐ°Ð·Ð°Ð½Ð° Ð°ÐºÑÑÐ°Ð»ÑÐ½Ð¾ÑÑÑ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ð¹ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ñ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð±Ð»Ð¾ÐºÐ¾Ð² Ð¼Ð¸ÐºÑÐ¾Ð¿ÑÐ¾ÑÐµÑÑÐ¾ÑÐ½Ð¾Ð¹ ÑÐµÐ»ÐµÐ¹Ð½Ð¾Ð¹ Ð·Ð°ÑÐ¸ÑÑ Ð´Ð»Ñ ÑÐµÐ»ÐµÐ¹ Ð¾ÑÐ³Ð°Ð½Ð¸Ð·Ð°ÑÐ¸Ð¸ ÑÐµÐ¼Ð¾Ð½ÑÐ¾Ð² Ð¿Ð¾ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ñ. Ð Ð°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ñ ÑÑÐµÐ´ÑÑÐ²Ð° ÑÐ·ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Python Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¾ÑÑÐ¸Ð»Ð»Ð¾Ð³ÑÐ°Ð¼Ð¼ ÐÐÐ Ð. ÐÐ¸Ð±Ð»Ð¸Ð¾ÑÐµÐºÐ¸, Ð´Ð¾ÑÑÑÐ¿Ð½ÑÐµ Ð¿ÑÐ¸ ÑÐ°Ð±Ð¾ÑÐµ Ñ ÑÐ·ÑÐºÐ¾Ð¼ Python (Pandas, MatPlotLib, NumPy, SciKit-Learn), Ð±ÑÐ»Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ñ Ð´Ð»Ñ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ "Ð¸Ð´ÐµÐ°Ð»ÑÐ½ÑÑ" ÑÑÐ°ÑÑÐºÐ¾Ð² Ð¾ÑÑÐ¸Ð»Ð»Ð¾Ð³ÑÐ°Ð¼Ð¼ ÐÐÐ Ð, Ð±Ð»Ð¸Ð·ÐºÐ¸Ñ Ðº ÑÐ¸Ð½ÑÑÐ¾Ð¸Ð´Ðµ, Ð¸ "Ð½ÐµÐ¸Ð´ÐµÐ°Ð»ÑÐ½ÑÑ" ÑÑÐ°ÑÑÐºÐ¾Ð² Ð¾ÑÑÐ¸Ð»Ð»Ð¾Ð³ÑÐ°Ð¼Ð¼, Ð½Ðµ ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²ÑÑÑÐ¸Ñ ÑÐ¸Ð½ÑÑÐ¾Ð¸Ð´Ðµ. ÐÐ¾ÐºÐ°Ð·Ð°Ð½Ð° ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¿ÑÐ¸ ÑÐµÑÐµÐ½Ð¸Ð¸ Ð´Ð°Ð½Ð½ÑÑ Ð·Ð°Ð´Ð°Ñ, Ð¿ÑÐ¸Ð¼ÐµÐ½Ð¸ÑÐµÐ»ÑÐ½Ð¾ Ðº Ð°Ð½Ð°Ð»Ð¸Ð·Ñ Ð¾ÑÑÐ¸Ð»Ð»Ð¾Ð³ÑÐ°Ð¼Ð¼ Ð±Ð»Ð¾ÐºÐ¾Ð² Ð¼Ð¸ÐºÑÐ¾Ð¿ÑÐ¾ÑÐµÑÑÐ¾ÑÐ½Ð¾Ð¹ ÑÐµÐ»ÐµÐ¹Ð½Ð¾Ð¹ Ð·Ð°ÑÐ¸ÑÑ: Ð¿ÑÐ¸Ð¼ÐµÑÑ ÑÐ°Ð±Ð¾ÑÑ Ñ Ð¼Ð¾Ð´ÐµÐ»ÑÑ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð¸ Ð¿ÑÐ°Ð²Ð¸Ð»ÑÐ½ÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð² 86% ÑÐ»ÑÑÐ°ÑÑ.
description: The relevance indicator for solving forecasting problems are the technical parameters of microprocessor protection units for the purpose of arranging repairs as they are. The Python programming language tools for solving the problems of classification of BMRZ oscillograms are considered. Libraries available when working with the Python language (Pandas, MatPlotLib, NumPy, SciKit-Learn) were used to determine the "ideal" sections of the BMRZ waveforms close to the sine wave and the "non-ideal" sections of the waveforms that do not correspond to the sinusoid. Performance indicators for using machine learning methods to solve these problems, as applied to the analysis of oscillograms of microprocessor relay protection blocks: examples of work with confirmed classification in 86% of cases.