Abstract
Positive Energy Districts (PED) require integration of different systems and infrastructures for the optimal interactions among buildings, stakeholders, mobility, energy systems and ICT systems. Digital twin is a coupled approach for new forms of modelling and analysis based on big data and machine learning/artificial intelligence, which combines capacities of virtual model, data management, analytics, simulation, system controls, visualization and information sharing. Digital twin is regarded as a potential solution to optimize PEDs. This chapter presents a comprehensive review about digital twins for PED from aspects of concepts, working principles, tools/platform and applications, in order to address the issues of both ‘how digital PED twin is made’ and ‘how digital PED twin optimizes liveability’. Further challenges and opportunities are brought forward for discussion. The outcome of the review is expected to provide useful information for optimizing the liveability of the urban environment in line with social, economic and environmental sustainability.
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Shen, J., Saini, P., Zhang, X. (2021). Machine Learning and Artificial Intelligence for Digital Twin to Accelerate Sustainability in Positive Energy Districts. In: Zhang, X. (eds) Data-driven Analytics for Sustainable Buildings and Cities. Sustainable Development Goals Series. Springer, Singapore. https://doi.org/10.1007/978-981-16-2778-1_20
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