Artificial Intelligence and a Digital Twin are effecting building energy Management

Using a three-dimensional data model connected to the Internet of Things, artificial intelligence, and machines, the study looks into the possibility of using digital twin-based techniques and methods to create an intelligent optimization and automation system to manage energy in a residential area....

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Bibliographic Details
Published in2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) pp. 1 - 8
Main Authors Deena, G., Gulati, Kamal, Jha, Rashmi, Bajjuri, Usha Rani, Sahithullah, M., Singh, Manjeet
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.07.2022
Subjects
Artificial Intelligence
classes
Data models
digital twin
Digital twins
Energy efficiency
Heuristic algorithms
IoT
Machine learning
Reliability engineering
Renewable energy sources
Solar energy
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Summary:Using a three-dimensional data model connected to the Internet of Things, artificial intelligence, and machines, the study looks into the possibility of using digital twin-based techniques and methods to create an intelligent optimization and automation system to manage energy in a residential area. The Rinascimento III neighbourhood in Rome is the focus of the case study. It has 16 eight-story buildings with 216 apartments that are powered by renewable energy for 70% of the time. Because the complex has dynamic analytic algorithms built in, many different energy-efficient intervention scenarios can be tested to make sure the real comfort and temperature levels of the complex are kept. At the same time, a reliable IT infrastructure based on advanced computing paradigms that can deliver data is being designed and put into place. To meet NZEB criteria, a method was created to analyse the efficiency and effectiveness of integrated systems for producing renewable energy from solar energy. This is needed to raise the threshold of self-produced energy (near zero energy buildings).
DOI:10.1109/ICSES55317.2022.9914233