title: ÐÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÑÑÐ¾ÑÐ½Ð¾Ð³Ð¾ Ð³ÑÐ°ÑÐ¸ÐºÐ° Ð½Ð°Ð³ÑÑÐ·ÐºÐ¸ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑ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: Forecasting the daily load schedule using a neural network
Creators: name: ÐÐ°Ð»ÑÐ³Ð¸Ð½Ð°, ÐÐºÐ°ÑÐµÑ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: 2021
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
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ÐÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÐµ Ð¼Ð°ÑÐ¸Ð½Ñ ÑÐ»ÐµÐºÑÑÐ¾Ð½Ð½ÑÐµ
Ð¢ÐµÐ¿Ð»Ð¾Ð²ÑÐµ ÑÐ»ÐµÐºÑÑÐ¸ÑÐµÑÐºÐ¸Ðµ ÑÑÐ°Ð½ÑÐ¸Ð¸
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description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¾ÑÐµÐ½ÐºÐµ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²ÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð² ÑÑÐµÐ´Ðµ MATLAB Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÑÑÐ¾ÑÐ½Ð¾Ð³Ð¾ ÑÐ»ÐµÐºÑÑÐ¾Ð¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ñ. ÐÐ°Ð´Ð°ÑÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐµÑÐ°Ð»Ð¸ÑÑ Ð² ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: Ð¾Ð¿Ð¸ÑÐ°Ð½Ð¸Ðµ Ð¿ÑÐ¸ÑÐ¸Ð½ Ð¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð½Ð°Ð³ÑÑÐ·ÐºÐ¸; Ð²ÑÐ±Ð¾Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸, ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð° Ð½ÐµÐ¹ÑÐ¾Ð½Ð¾Ð² Ð¸ ÑÐºÑÑÑÑÑ ÑÐ»Ð¾ÐµÐ²; Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ðµ Ð¼Ð°ÑÑÐ¸Ð²Ð° Ð²ÑÐ¾Ð´Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð´Ð»Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸; Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð½Ð° 80% Ð¸Ð¼ÐµÑÑÐ¸ÑÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð¸ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¿ÑÐ°Ð²Ð¸Ð»ÑÐ½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ Ð½Ð° Ð¾ÑÑÐ°Ð²ÑÐ¸ÑÑÑ 20%; Ð°Ð½Ð°Ð»Ð¸Ð· Ð¾ÑÐ¸Ð±ÐºÐ¸ Ð´Ð°Ð½Ð½ÑÑ, Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ Ð¾Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. ÐÐ°Ð½Ð½ÑÐµ Ð¾ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð¸ Ð¸ Ð¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ð¸ ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÐµÑÐ³Ð¸Ð¸ Ð±ÑÐ»Ð¸ Ð²Ð·ÑÑÑ Ñ ÑÐ°Ð¹ÑÐ° Â«Ð¡Ð¸ÑÑÐµÐ¼Ð½Ð¾Ð³Ð¾ Ð¾Ð¿ÐµÑÐ°ÑÐ¾ÑÐ° ÐÐ´Ð¸Ð½Ð¾Ð¹ ÑÐ½ÐµÑÐ³ÐµÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐ¸ÑÑÐµÐ¼ÑÂ». ÐÑÐ»Ð¾ Ð¿ÑÐ¸Ð½ÑÑÐ¾ ÑÐµÑÐµÐ½Ð¸Ðµ Ð¿Ð¾Ð´Ð°Ð²Ð°ÑÑ Ð½Ð° Ð²ÑÐ¾Ð´ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ ÑÐµÐ¼Ð¿ÐµÑÐ°ÑÑÑÑ Ð¾ÐºÑÑÐ¶Ð°ÑÑÐµÐ¹ ÑÑÐµÐ´Ñ, Ð´ÐµÐ½Ñ Ð½ÐµÐ´ÐµÐ»Ð¸, ÐºÐ°Ð»ÐµÐ½Ð´Ð°ÑÐ½ÑÐ¹ Ð´ÐµÐ½Ñ, Ð° ÑÐ°ÐºÐ¶Ðµ ÑÑÐ¸ÑÑÐ²Ð°ÑÑ ÑÑÐµÐ½Ð´ Ð¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ñ, Ð²Ð¾Ð·ÑÐ°ÑÑÐ°ÑÑÐ¸Ð¹ ÑÐ¾ Ð²ÑÐµÐ¼ÐµÐ½ÐµÐ¼. ÐÐ° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð³Ð¾Ð´Ð¾Ð²ÑÑ, Ð½ÐµÐ´ÐµÐ»ÑÐ½ÑÑ Ð¸ ÑÐµÐ¼Ð¿ÐµÑÐ°ÑÑÑÐ½ÑÑ ÐºÐ¾ÑÑÑÐ¸ÑÐ¸ÐµÐ½ÑÐ¾Ð² Ð±ÑÐ» ÑÐ¾ÑÑÐ°Ð²Ð»ÐµÐ½ Ð¼Ð°ÑÑÐ¸Ð² Ð´Ð°Ð½Ð½ÑÑ Ð¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÐºÐ°Ð¶Ð´Ð¾Ð³Ð¾ Ð´Ð½Ñ Ð´Ð²Ð°Ð´ÑÐ°ÑÐ¸ Ð»ÐµÑ. ÐÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð¿ÑÐ¸ ÑÐ°Ð·Ð½Ð¾Ð¼ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð¾Ð² Ð² ÑÐ»Ð¾ÑÑ Ð¸ Ð¿ÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð° Ð¾ÑÐ¸Ð±ÐºÐ° Ð²ÑÑÐ¾Ð´Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÑÐ°Ð±Ð¾ÑÑ Ð¿Ð¾Ð»ÑÑÐµÐ½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð° ÑÐ»ÐµÐºÑÑÐ¾Ð¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ñ Ð¸ Ð¾ÑÐµÐ½ÐµÐ½Ð° ÐµÐ³Ð¾ ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¸ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ.
description: An important parameter for the functioning of the power system is the balance of generated and consumed electricity. The given work is devoted to assessing the possibility of using neural network algorithms in the MATLAB environment for predicting daily power consumption. The research set the following goals: description of the reasons and methods of forecasting the load; choice of neural network architecture, number of neurons and hidden layers; building an array of input data for training and testing a neural network in the time interval of consumption from 2000 to 2020, using real data; training the neural network on 80% of the available data and testing the correct operation on the remaining 20%; analysis of the error of the data received from the neural network. The data on electricity generation and consumption were taken from the website of the System Operator of the Unified Energy System. It was decided to feed the input of the neural network with the ambient temperature, day of the week, calendar day, and also take into account the consumption trend that increases with time. On the basis of annual, weekly and temperature coefficients, an array of consumption data was compiled for each day of twenty years. The training of the neural network with a different number of its layers was carried out and the error of the output data was analyzed. As a result of the work, an algorithm for forecasting power consumption was obtained and its accuracy and efficiency were evaluated.