title: Ð ÐµÐ³ÑÐµÑÑÐ¸Ð¾Ð½Ð½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð² ÑÐ½ÐµÑÐ³ÐµÑÐ¸ÐºÐµ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Regression analysis in the energy sector using machine learning
Creators: name: ÐÐ°Ð·Ð°Ð½Ð¸Ð½, ÐÐ¼Ð¸ÑÑÐ¸Ð¹
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: 2024
Subjects: ÑÐµÐ³ÑÐµÑÑÑÐ¸Ñ
Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð²
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
prophet
sarima
ÑÐ½ÐµÑÐ³ÐµÑÐ¸ÐºÐ°
regression
time series forecasting
machine learning
energy in-dustry

description: Ð¦ÐµÐ»ÑÑ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð½Ð°Ð³ÑÑÐ·ÐºÐ¸(Ð²ÑÑÐ°Ð±Ð¾ÑÐºÐ¸) ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÐµÑÐ³Ð¸Ð¸ Ð¸ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ðµ ÑÐ¸ÑÑÐµÐ¼Ñ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð½ÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¹. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¸ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ñ Ð´Ð²Ðµ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ: Prophet Ð¸ SARIMA. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¸ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ñ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÑ Ð¿Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð½Ð° Ð´Ð°Ð½Ð½ÑÑ, Ð²Ð·ÑÑÑÑ Ð¸Ð· Ð¾ÑÐºÑÑÑÑÑ Ð¸ÑÑÐ¾ÑÐ½Ð¸ÐºÐ¾Ð². ÐÑÐ»Ð¾ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ Ð¿Ð¾ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð¸ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð¸, ÑÑÐ¾ Ð½ÐµÐºÐ¾ÑÐ¾ÑÑÐµ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ, Ð² Ð·Ð°Ð²Ð¸ÑÐ¸Ð¼Ð¾ÑÑÐ¸ Ð¾Ñ Ð½Ð°ÑÑÑÐ°Ð¸Ð²Ð°ÐµÐ¼ÑÑ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð², Ð¸Ð¼ÐµÑÑ Ð»ÑÑÑÐ¸Ð¹ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ, Ð½ÐµÐ¶ÐµÐ»Ð¸ Ð´ÑÑÐ³Ð¸Ðµ. ÐÑÐ» Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ Ð¿ÑÐµÐ´Ð²Ð°ÑÐ¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð°Ð½Ð½ÑÑ, Ñ ÑÐµÐ»ÑÑ ÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑÐµÐ³Ð¾ Ð²Ð¸Ð´Ð° dataset. ÐÐ° Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ, Ð±ÑÐ»Ð¸ Ð¾Ð±ÑÑÐµÐ½Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐ·ÑÐºÐ° Python Ð¸ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐº, ÑÐ°ÐºÐ¸Ñ ÐºÐ°Ðº: Keras Ð¸ Tensorflow, statsmodels, sklearn Ð¸ Ð´ÑÑÐ³Ð¸Ñ.
description: The purpose of this work is to study various ma-chine learning models for predicting the load (generation) of electricity and to build a forecasting system based on the research conducted. In the course of the work, two architectures of machine learning models were considered: Prophet and SARIMA. In the course of the work, experiments were conducted to train models on data taken from open sources. A comparison was made on the effectiveness and accuracy of forecasting. The results showed that some architectures, depending on the configurable parameters, have a better predictive accuracy index than others. A preliminary analysis of the data was carried out in order to form a suitable dataset. Based on the data obtained, models were trained using the Python language and various libraries such as Keras and Tensorflow, statsmodels, sklearn and others.