University of Waikato
University of Waikato is receiving funding from 2 SSIF platforms – Advanced Energy Technology and Data Science. On this page are the public statements from our contracts with University of Waikato describing the programmes being funded.
Ahuora: Delivering sustainable industry through smart process heat decarbonisation
University of Waikato is receiving $12.5 million over 7 years to deliver the Advanced Energy Technology programme “Ahuora: Delivering sustainable industry through smart process heat decarbonisation”. The following is the public statement for this programme from our contract with University of Waikato.
Our vision is to create a new Adaptive Digital Twin energy-technology platform (Ahuora), underpinned by the next-generation of energy systems science, that will be accessible to engineering researchers, service providers and industrial site-owners and is essential to decarbonise the process heat sector. This sector contributes 28% of NZ’s energy emissions but represents arguably the most complex and challenging of the energy sectors to decarbonise by 2050, as legislated by the Climate Change Response (Zero Carbon) Act 2019.
Research over the past several decades has shown that decarbonisation of the process heat sector cannot be solved sustainably by a single “silver bullet” energy technology; rather, it demands a range of technologies integrated with an industrial site to form a unified energy system utilising renewable energy. Developing effective solutions involves knowing when, where and how to apply the numerous emerging and mature energy technologies in the most synergistic way, while having a minimum adverse effect on production and managing energy supply and demand volatilities.
A net-zero-carbon process heat sector will require highly integrated, productive and efficient systems that encompass both the industrial site as well as neighbouring industries, renewable resources and communities.
Our team of Waikato, Auckland and Massey University researchers will deliver the energy systems technology and build the Ahuora platform to assist in re-engineering the way we use, convert, provision and store energy for process heat using a smart systems approach. This engineering research programme will transform the underpinning energy systems science, embed the new technology in an advanced digital platform, and produce world-class engineering leaders in energy systems.
The new platform’s name, Ahuora, gifted by Associate Professor Te Taka Keegan of the University of Waikato, combines the Māori words: ‘ahu’ meaning ‘to fashion’ and ‘ora’ meaning ‘healthy’, and represents our goal – sustainable industry for Aotearoa New Zealand.
Time-Evolving Data Science / Artificial Intelligence for Advanced Open Environmental Science
University of Waikato is receiving $13 million over 7 years to deliver the Data Science programme “Time-Evolving Data Science / Artificial Intelligence for Advanced Open Environmental Science”. The following is the public statement for this programme from our contract with University of Waikato.
Data are essential to research, understand, set policy for and manage New Zealand’s environment, but environmental data presents many challenges that require new data science methods to overcome them, and a substantial increase in the capability of environmental researchers, governors and managers to use data science in their work. This programme will develop those new methods and build the required capability.
In particular, we will focus on developing methods to deal with environmental datasets that are collected in large volumes over time, and must therefore be dealt with as streams that are analysed incrementally, as they are measured, rather than as collections of data that can be analysed all at once. These methods will address underlying characteristics of the data that evolve over time (e.g. due to climatic or ecological changes), and data that are collected at a range of time intervals and spatial scales ranging from broadscale satellite images to singlepoint measurements on the ground, in the water or air. The methods we develop will be interpretable and explainable (to help users understand why an algorithm produces some particular output), identify and understand anomalies (to distinguish “normal” from “unusual” measurements) and quantify uncertainty in algorithm output (to help decision-makers understand how confident they can be in conclusions drawn from the data science methods).
To deliver the methods we develop in a form that environmental scientists and managers can use, we will build a new open source framework to do machine learning on time series data, and provide an open access repository of environmental datasets to improve reproducibility in environmental data science. Through workshops, undergraduate and postgraduate research projects within the programme, we will build New Zealand’s capability in fundamental and applied data science relevant to environmental data, from introductory to postdoctoral level.