Established in June 2010, the Energy Research Institute @NTU (ERI@N) distinguishes itself through excellence in basic research directed towards outcomes of high industry relevance, with focus on systems-level research for tropical megacities.

It achieves this via seven Interdisciplinary Research Programmes (IRP) and two Flagship Programmes that covers the energy value chain from generation, transmission to end use. The IRPs address research covering the domains of renewables, low carbon generation along with energy storage and fuel cells; renewables integration, multi-energy systems and grids; sustainable buildings, future mobility solutions and maritime clean energy.

The two flagship projects, EcoCampus and Renewable Energy Integration Demonstrator Singapore (REIDS) serve as strong “Living Lab” platforms to engage industry developed innovation, focusing on solutions that achieves energy efficiency and renewable energy integration into smart micro grids, respectively.

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Sun Lu(Nanyang Technological University)
Sun Lu logo
Nov 23, 2022
His research interests mainly include robust statistics, robust control systems, adaptive control systems, distributed control systems and their applications in power systems.
Comma Separated Values - 4.3 MB - MD5: c66b861ce124de18644ee743a31be29e
Video of the drive ( Circuit drive at CETRAN test track)
MPEG-4 Video - 160.8 MB - MD5: 3a33d4074b0d1b211dffaba9121c36fe
Sample of a drive collected at CETRAN Test track by the vehicle demonstrator
Comma Separated Values - 288.6 MB - MD5: 7c2f72b472f675d7b157ca1c135ef952
Sample of a public Road data collected by the vehicle demonstrator.
MPEG-4 Video - 1.4 GB - MD5: a13b6c3e9e14426d972f927bbeef3d93
Public road drive video
MPEG-4 Video - 185.1 MB - MD5: bbdba28b0977276494d53c9643e67c13
Video of the public road drive X20
Sep 28, 2022CETRAN
Dataverse for LTA funded project Research & Development of Methods for a Holistic Evaluation of AV Safety under Tropical Urban Conditions under UMGC Grant L010.
1i
Unknown - 32.0 KB - MD5: fc2d19408fc4ea935a37bcec05576cb3
Unknown - 32.0 KB - MD5: 481dbe489c2146d22482c6f03daf7abc
Unknown - 128.0 KB - MD5: a665a172f350b5f28101bad1b641ffb7
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