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Title: Harvesting 3D multiphysics modeling techniques for smart and sustainable university campus
Authors: Zhang, Xiaoqin
Thomas, Rithika
Jadhav, Nilesh
Lee, Jimmy
Conaghan, Catherine
Rawte, Rohan
Mehta, Priyanka
Keywords: Energy Modeling
DRNTU::Engineering::Mechanical engineering::Energy conservation
Sustainable Campus
Issue Date: 2017
Source: Mehta, P., Zhang, X., Thomas, R., Jadhav, N., Lee, J., Conaghan, C., & Rawte, R. (2017). Harvesting 3D Multiphysics Modeling Techniques for Smart and Sustainable University Campus. Energy Procedia, 143, 851-858. doi:10.1016/j.egypro.2017.12.773
Series/Report no.: Energy Procedia
Abstract: The EcoCampus Initiative is a novel flagship RD&D programme built on applied research and test-bedding of innovative technologies. The initiative covers the whole of Nanyang Technological University’s (NTU) 200-hectare campus and adjoining 50 hectares JTC Corporation’s CleanTech Park. The initiative was launched with a mission to achieve an impactful target of a 35% reduction in energy, water and waste intensity by 2020 (baseline, 2011), while leading the development and adoption of innovative technologies. The initiative has successfully collaborated with more than 20 industries and provided them the campus as testbed. The challenge ahead is to scrutinize the tested technologies, determine the best performing ones and find out the most optimum scale and location for their deployment. To aid such decision making, EcoCampus has collaborated with IES (Integrated Environmental Solutions) to exploit their IESVE™ technology. The project aims at developing a Multiphysics 3D Virtual Model of the campus that can virtually simulate the technologies through advanced Multiphysics modelling techniques. In addition to the technology performance analysis, IES’ innovative process solution, Ci2 (Collect, Investigate, Compare, Invest), is deployed to optimize the building performance in NTU Campus. The process focusses on Collecting historical data and identifying data gaps. This is followed by Investigating data using advanced IESVE™ analytics. This stage provides insights that may be hidden within the existing data. Once the story behind the data is known, the data is Compared against a calibrated model. Benchmarking analysis, decisions on refurbishing etc. are made during this stage. ROI, payback periods, NPV etc., prediction is also made in ‘Compare’ stage. Lastly, the Invest stage helps the decision makers to determine and prioritise the most attractive investment solutions for a sustainable and energy efficient operational building. This paper focuses on requirements for setting up a design model and modifying it to understand building behaviour.
ISSN: 1876-6102
DOI: 10.1016/j.egypro.2017.12.773
Research Centres: Energy Research Institute @ NTU (ERI@N) 
Rights: © 2017 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ERI@N Journal Articles

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