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 in2024 IEEE Conference on Pervasive and Intelligent Computing (PICom) pp. 83 - 89
Main Authors Islam, Md Babul, Guerrieri, Antonio, Gravina, Raffaele, Fortino, Giancarlo
Format Conference Proceeding
LanguageEnglish
Published IEEE 05.11.2024
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DOI10.1109/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.
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
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  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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StartPage 83
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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