Heritage Conservation and Cultural Preservation

Improving energy efficiency while preserving historic buildings with digital twins and artificial intelligence

Zhongjun Ni, Petra Eriksson, Yu Liu, Magnus Karlsson and Shaofang Gong

Published 2021

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📄 Paper
This study proposes a digitalization framework for historic buildings. In this framework, advanced techniques, like Internet of Things (IoT), cloud computing, and artificial intelligence (AI), are utilized to create digital twins for historic buildings. A digital twin is a software representation of a physical object. This study uses digital twins to protect, predict, and optimize through analytics of real-time and historical data of selected features. Heterogeneous data of historic buildings, such as indoor environment, energy consumption metering, and outdoor climate, are collected with proper sensors or retrieved from other data sources. Then, these data are periodically uploaded and stored in the database of the cloud platform. Based on these data, AI models are trained through appropriate machine learning algorithms to monitor historic buildings, predict energy consumption, and control energy-consuming equipment autonomously to reach the balance of energy efficiency, building conservation, and human comfort. The cloud-based characteristic of our digitalization framework makes the digital twins developed in this study easy to be transplanted to many other historic buildings in Sweden and other countries.

Abstract

This study proposes a digitalization framework for historic buildings. In this framework, advanced techniques, like Internet of Things (IoT), cloud computing, and artificial intelligence (AI), are utilized to create digital twins for historic buildings. A digital twin is a software representation of a physical object. This study uses digital twins to protect, predict, and optimize through analytics of real-time and historical data of selected features. Heterogeneous data of historic buildings, such as indoor environment, energy consumption metering, and outdoor climate, are collected with proper sensors or retrieved from other data sources. Then, these data are periodically uploaded and stored in the database of the cloud platform. Based on these data, AI models are trained through appropriate machine learning algorithms to monitor historic buildings, predict energy consumption, and control energy-consuming equipment autonomously to reach the balance of energy efficiency, building conservation, and human comfort. The cloud-based characteristic of our digitalization framework makes the digital twins developed in this study easy to be transplanted to many other historic buildings in Sweden and other countries.

Topics

AI for Energy Performance OptimizationPredictive Conservation and Risk Assessmentconservation AIdamage detectiondigital twinshuman-centric IoTmaterial degradation predictionpredictive maintenancerisk modelingsensor data integrationstructural health monitoring

Cite this paper

@article{0d66eda4,
doi = {10.1088/1755-1315/863/1/012041},
url = {https://doi.org/10.1088/1755-1315/863/1/012041},
year = {2021},
month = {oct},
publisher = {IOP Publishing},
volume = {863},
number = {1},
pages = {012041},
author = {Ni, Zhongjun and Eriksson, Petra and Liu, Yu and Karlsson, Magnus and Gong, Shaofang},
title = {Improving energy efficiency while preserving historic buildings with digital twins and artificial intelligence},
journal = {IOP Conference Series: Earth and Environmental Science},
abstract = {This study proposes a digitalization framework for historic buildings. In this framework, advanced techniques, like Internet of Things (IoT), cloud computing, and artificial intelligence (AI), are utilized to create digital twins for historic buildings. A digital twin is a software representation of a physical object. This study uses digital twins to protect, predict, and optimize through analytics of real-time and historical data of selected features. Heterogeneous data of historic buildings, such as indoor environment, energy consumption metering, and outdoor climate, are collected with proper sensors or retrieved from other data sources. Then, these data are periodically uploaded and stored in the database of the cloud platform. Based on these data, AI models are trained through appropriate machine learning algorithms to monitor historic buildings, predict energy consumption, and control energy-consuming equipment autonomously to reach the balance of energy efficiency, building conservation, and human comfort. The cloud-based characteristic of our digitalization framework makes the digital twins developed in this study easy to be transplanted to many other historic buildings in Sweden and other countries.}
}