Digital Twins for Smart Cities: Case Study and Visualisation via Mixed Reality

William Piper, Hongjian Sun, Jing Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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Digital twins is an increasingly valuable technology for realising smart cities worldwide. Visualising this technology using mixed reality creates unprecedented opportunities to easily access relevant data and information. In this paper, a digital twins-based system is designed to visualise information from a city’s street lighting system. Data is obtained in two ways: from measured parameters of a miniature model street light in realtime, and from real Durham street lighting. Machine learning is used to maximise the efficiency of purchasing electricity from the grid, and to forecast appropriate adaptive street light brightness levels based on city’s traffic flow and solar irradiance. An application designed in Unity Pro is deployed on a Microsoft HoloLens 2, and it allows the user to view the processed data and control the model street light. It was found that the application performed as desired, displaying information such as voltage, current, carbon emission, electricity price, battery state of charge and LED mode, while enabling control over the model street light. Moreover, the Deep Q-Network machine learning algorithm successfully scheduled to buy electricity at times of low price and low carbon intensity, while the Long Short-Term Memory algorithm accurately forecasted traffic flow with mean RootMean-Square Error and Mean Absolute Percentage Error values of 12.0 and 20.0 respectively.
Original languageEnglish
Title of host publication2022 IEEE 96th Vehicular Technology Conference
Subtitle of host publication(VTC2022-Spring)
Place of PublicationPiscataway, US
Number of pages5
ISBN (Electronic)978-1-6654-5469-8
ISBN (Print)978-1-6654-5468-1
Publication statusPublished - 2022
Event 2022 IEEE 96th Vehicular Technology Conference: VTC2022-Fall - Imperial College of London, London: Beijing, United Kingdom
Duration: 26 Sept 202229 Sept 2022


Conference 2022 IEEE 96th Vehicular Technology Conference
Country/TerritoryUnited Kingdom
CityLondon: Beijing
Internet address


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