A Deep Learning Based Digital Twin for Indoor Temperature Prediction in Smart Buildings
With the advent of the Internet of Things (IoT), smart buildings (SBs) are increasingly equipped with a variety of sensors, including those for temperature, humidity, lighting, and human presence. These SBs often incorporate automated heating, ventilation, and air conditioning (HVAC) systems that ge...
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Published in | 2024 IEEE Conference on Pervasive and Intelligent Computing (PICom) pp. 83 - 89 |
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Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
05.11.2024
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Subjects | |
Online Access | Get full text |
DOI | 10.1109/PICom64201.2024.00018 |
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Abstract | With the advent of the Internet of Things (IoT), smart buildings (SBs) are increasingly equipped with a variety of sensors, including those for temperature, humidity, lighting, and human presence. These SBs often incorporate automated heating, ventilation, and air conditioning (HVAC) systems that generate substantial amounts of data daily. This data can be leveraged to develop and optimize energy-saving strategies. In this context, accurate indoor temperature prediction is essential for effective energy management. Moreover, if used properly, this prediction task can also improve the comfort within SBs. This paper proposes a novel approach to predicting indoor temperature called "Digital Twin for Temperature Prediction (DT4TP)" based on the digital twin paradigm and deep learning (DL) LSTM and BilSTM models. DT4TP has been designed to process real-time and historical data from various IoT sensors in SBs during the winter and summer seasons and to predict the temperature in the future in such environments. DT4TP has been validated through a case study developed at ICAR-CNR in Rende, Italy, and through a state-of-the-art dataset collected from office rooms in Hebei, China. Furthermore, this paper compares the proposed approach with well-known algorithms such as ANN, DNN, CNN, LSTM, and GRU. The comparison uses metrics such as MAE, MSE, RMSE, and R^{2} . The proposed approach can improve the temperature management of the HVAC system offering benefits for SB operations. |
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AbstractList | With the advent of the Internet of Things (IoT), smart buildings (SBs) are increasingly equipped with a variety of sensors, including those for temperature, humidity, lighting, and human presence. These SBs often incorporate automated heating, ventilation, and air conditioning (HVAC) systems that generate substantial amounts of data daily. This data can be leveraged to develop and optimize energy-saving strategies. In this context, accurate indoor temperature prediction is essential for effective energy management. Moreover, if used properly, this prediction task can also improve the comfort within SBs. This paper proposes a novel approach to predicting indoor temperature called "Digital Twin for Temperature Prediction (DT4TP)" based on the digital twin paradigm and deep learning (DL) LSTM and BilSTM models. DT4TP has been designed to process real-time and historical data from various IoT sensors in SBs during the winter and summer seasons and to predict the temperature in the future in such environments. DT4TP has been validated through a case study developed at ICAR-CNR in Rende, Italy, and through a state-of-the-art dataset collected from office rooms in Hebei, China. Furthermore, this paper compares the proposed approach with well-known algorithms such as ANN, DNN, CNN, LSTM, and GRU. The comparison uses metrics such as MAE, MSE, RMSE, and R^{2} . The proposed approach can improve the temperature management of the HVAC system offering benefits for SB operations. |
Author | Islam, Md Babul Gravina, Raffaele Fortino, Giancarlo Guerrieri, Antonio |
Author_xml | – sequence: 1 givenname: Md Babul surname: Islam fullname: Islam, Md Babul email: mdbabul.islam@dimes.unical.it organization: University of Calabria,Rende,CS,Italy – sequence: 2 givenname: Antonio surname: Guerrieri fullname: Guerrieri, Antonio email: antonio.guerrieri@icar.cnr.it organization: ICAR-CNR,Rende,CS,Italy – sequence: 3 givenname: Raffaele surname: Gravina fullname: Gravina, Raffaele email: r.gravina@dimes.unical.it organization: University of Calabria,Rende,CS,Italy – sequence: 4 givenname: Giancarlo surname: Fortino fullname: Fortino, Giancarlo email: g.fortino@unical.it organization: University of Calabria,Rende,CS,Italy |
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Snippet | With the advent of the Internet of Things (IoT), smart buildings (SBs) are increasingly equipped with a variety of sensors, including those for temperature,... |
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SubjectTerms | Deep learning Digital Twin Digital twins HVAC HVAC Control Intelligent sensors Internet of Things LSTM Measurement Predictive models Real-time systems Smart Building Smart buildings Temperature sensors |
Title | A Deep Learning Based Digital Twin for Indoor Temperature Prediction in Smart Buildings |
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