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Developing More Efficient Technologies for Smart Grids and Smart Homes

Image source: ntu.edu.sg

An established technology enterprise will be working with the Energy Research Institute at NTU Singapore (ERI@N) to address industry demands for advanced technologies that can help to save energy and cost, improve the efficiency, flexibility and resilience of power grids and reduce Singapore’s carbon footprint.

These digital solutions include new smart grid physical systems, a smart energy router for power grid support, a smart home energy management platform as well as a Data Fusion Software Platform that combines Artificial Intelligence and Big Data for use in smart home applications and power grids. The two parties have inked a four-year agreement to seal the partnership, which is in line with the company’s global strategy to invest and provide new value to its business partners in the emerging industry megatrends: Optoelectronics, Cloud Computing, 5G and AIoT, EV Charging and Smart Grid technologies.

NTU Singapore’s partnership to develop innovative technologies aims to bring wide-ranging benefits for tomorrow’s homes and societies. The University recently unveiled its 15-year Sustainability Manifesto which aspires to achieve carbon neutrality by 2035, and this collaboration is one of many partnerships that enables NTU to play a key role in building a more resilient and sustainable environment. It is also an example of the University’s continued push for translational research, by working closely with the industry to ensure that our research outcomes can lead to significant commercial impact.

– Professor Lam Khin Yong, Senior Vice President (Research), NTU Singapore

The four key projects arising from this partnership are:

  1. Development of smart grid physical systems – high-frequency bi-directional inverter and converter with Silicon Carbide (SiC) technology – that will withstand higher temperatures and increase energy efficiency and power density.
  2. Creating a smart energy router to flexibly manage the power flow among renewable energy sources, energy storage and electric vehicles, making power grids smarter with energy intelligence. This will enable improved power quality and a reliable, cost-efficient, safe and sustainable grid operation.
  3. Designing a smart home energy management platform for the consumer electricity market that leverages data to drive energy savings and carbon reduction.
  4. Leveraging data fusion to share and visualise information comprehensively to accelerate the process of integrating technology across cross-disciplinary applications. This new Data Fusion Software Platform aims to meet the needs of an evolving range of unique global market pressures and challenges from decarbonisation to digitalisation and intelligence. Specifically, the platform aims to increase reliability, security and energy efficiency in homes and power grid operations, reduce operational costs and impact on the environment.

The collaboration with NTU Singapore is another important step towards designing technological solutions of the future and providing the smart grid and smart home energy management infrastructure needed to advance a connected living, working, and entertainment space.

As reported by OpenGov Asia, a team of scientists from NTU has developed a predictive computer programme that could be used to detect individuals who are at increased risk of depression. In trials using data from groups of depressed and healthy participants, the programme achieved an accuracy of 80% in detecting those individuals with a high risk of depression and those with no risk.

Powered by machine learning, the programme, named the Ycogni model, screens for the risk of depression by analysing an individual’s physical activity, sleep patterns, and circadian rhythms derived from data from wearable devices that measure his or her steps, heart rate, energy expenditure, and sleep data.

Over the next year, the team hopes to explore the impact of smartphone usage on depressive symptoms and the risk of developing depression by enriching their model with data on smartphone usage. This includes how long and frequent individuals use their mobile phones, as well as their reliance on social media.

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