title: ÐÐ¾ÑÑÑÐ¾ÐµÐ½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ñ ÑÐ»ÐµÐºÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐ½ÐµÑÐ³Ð¸Ð¸ Ð² Ð·Ð´Ð°Ð½Ð¸Ð¸
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: Building a model for predicting electrical energy consumption in a building
Creators: name: Ð ÑÐ±Ð°Ð½Ð¾Ð²Ð°, ÐÐ°Ð»ÐµÑÐ¸Ñ
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: Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÑÐ°Ð½ÑÑÐ¾ÑÐ¼ÐµÑÑ
Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ðµ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð²
Ð¿ÑÐ°ÐºÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ
LSTM
CNN
neural networks
transformers
time-series forecast
practical usage

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ Ð¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ñ ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÐµÑÐ³Ð¸Ð¸ Ð² Ð¾ÑÐ¸ÑÐ½Ð¾Ð¼ Ð·Ð´Ð°Ð½Ð¸Ð¸ Ð² Ð³Ð¾ÑÐ¾Ð´Ðµ ÐÐ°Ð½Ð³ÐºÐ¾Ðº, Ð¢Ð°Ð¸Ð»Ð°Ð½Ð´, Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÑ, ÑÐµÐºÑÑÐµÐ½ÑÐ½ÑÑ Ð¸ ÑÑÐ°Ð½ÑÑÐ¾ÑÐ¼ÐµÐ½Ð½ÑÑ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑ. Ð ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð´Ð»Ñ Ð´Ð¾ÑÑÐ¸Ð¶ÐµÐ½Ð¸Ñ Ð¿Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½Ð¾Ð¹ ÑÐµÐ»Ð¸ ÑÐµÑÐ°Ð»Ð¸ÑÑ ÑÐ»ÐµÐ´ÑÑÑÐ¸Ðµ Ð·Ð°Ð´Ð°ÑÐ¸: 1. ÐÐ·ÑÑÐµÐ½Ð¸Ðµ Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²ÑÑ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑ LSTM, CNN-LSTM, Transformer. 2. ÐÑÐ¸ÑÑÐºÐ° Ð¸ Ð¿Ð¾Ð´Ð³Ð¾ÑÐ¾Ð²ÐºÐ° Ð´Ð°Ð½Ð½ÑÑ Ðº ÑÐ°Ð±Ð¾ÑÐµ, Ð¸Ñ Ð°Ð½Ð°Ð»Ð¸Ð·. 3. ÐÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ðµ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ¾Ð² Ð´Ð»Ñ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð°. 4. ÐÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ðµ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ»ÐµÐºÑÑÐ¾Ð¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ñ Ð² Ð·Ð´Ð°Ð½Ð¸Ð¸. Ð ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð±ÑÐ»Ð¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ñ Ð¸ ÑÑÐ°Ð²Ð½ÐµÐ½Ñ ÑÑÐ¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ½ÐµÑÐ³Ð¾Ð¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ñ Ð·Ð´Ð°Ð½Ð¸Ñ, Ð¸ Ð»ÑÑÑÐ°Ñ Ð¸Ð· Ð½Ð¸Ñ Ð±ÑÐ»Ð° Ð²ÑÐ´ÐµÐ»ÐµÐ½Ð° Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½Ð¾Ð³Ð¾ ÑÐµÑÐµÐ½Ð¸Ñ. ÐÑÐ» ÑÐ´ÐµÐ»Ð°Ð½ Ð²ÑÐ²Ð¾Ð´ Ð¾ Ð¿ÑÐ¸Ð¼ÐµÐ½Ð¸Ð¼Ð¾ÑÑÐ¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð´Ð°Ð½Ð½Ð¾Ð¹ Ð·Ð°Ð´Ð°ÑÐ¸. ÐÐ¾Ð»ÑÑÐµÐ½Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¼Ð¾Ð³ÑÑ Ð±ÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ñ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ½ÐµÑÐ³Ð¾Ð¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ñ Ð² Ð·Ð´Ð°Ð½Ð¸ÑÑ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¸ÑÑÐ¾ÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ, ÑÑÐ¾ Ð¿Ð¾Ð·Ð²Ð¾Ð»Ð¸Ñ ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÐµÐµ ÑÐ¿ÑÐ°Ð²Ð»ÑÑÑ ÑÐ½ÐµÑÐ³ÐµÑÐ¸ÑÐµÑÐºÐ¸Ð¼Ð¸ ÑÐµÑÑÑÑÐ°Ð¼Ð¸.
description: This work is devoted to the creation of a machine learning model to solve the problem of predicting energy consumption in an office building in Bangkok, Thailand, using convolutional, recurrent and transformer architectures. During the study, to achieve this goal, the following tasks were solved: 1. Study and implementation of LSTM, CNN-LSTM, transformer neural network architectures. 2. Cleaning and preparing data for work, data analysis. 3. Conducting experiments to test the proposed approach. 4. Assessing the effectiveness of the model for predicting energy consumption in a building. The study developed and compared three building energy forecasting models and identified the best one as the optimal solution. The conclusion was made about the applicability of machine learning methods for this task. The results obtained can be used to predict energy consumption in buildings based on historical data, which will allow more efficient management of energy resources.