About
A new arsenal of artificial intelligence and data science tools will unlock massive energy savings and help UK business in their goal of achieving net zero.
These cutting-edge algorithms will automatically and continuously sift through a deluge of data and find new insights and recommend ways to slash energy consumption.
Crucially, these new tools will be designed and developed to be transferred across a wide range of business sectors and organisations, and across different buildings and infrastructure. This will pave the way for the ‘digital replication’ of energy efficiency savings, and a viral spread of the knowledge and techniques across sectors.
This multi-disciplinary programme, called Net0Insights, which is led by researchers at Lancaster University working alongside major industry partners, draws on Lancaster University’s Data Science Institute, bringing together researchers from School of Computing and Communications, Mathematics and Statistics, and Lancaster Environment Centre.
Aims
1. Develop automated techniques for supporting analysis, identifying and recommending energy savings strategies, based on the application of statistical and machine learning techniques to fine-grained energy data;
2. Derive knowledge of how, where, and when energy is used, to identify opportunities to reduce and shift demand by comparing differences in energy use over time within and between premises;
3. Support regular and repeated analysis, towards a continual improvement in energy reduction over time.
4. Provide open source, permissively licensed implementations for enabling uptake, even beyond our project partners and their partner networks.
Our publication and publicity strategies will maximise exposure of our project results to various stakeholder groups including academia, practitioners, and key industry stakeholders.
Team
Adrian Friday, Oliver Bates, Christian Remy, Adam Tyler, and Christina Bremer
School of Computing and Communications, Lancaster University
Idris Eckley, Paul Smith, Guillermo Cuauhtemoctzin Granados Garcia, Alex Gibberd, and Tak-Shing Chan
Mathematics and Statistics, Lancaster University
Ally Gormally-Sutton
Lancaster Environment Centre (LEC), Lancaster University
Collaborators
In collaboration with commercial partners Tesco, BT, BEST, and Lancaster University Facilities
Outputs
- Tyler, A., Bates, O., Friday, Adrian, and Remy, C. 2024, June. Mind the gap! The role of ICT in office heating & comfort. ICT4S.
- Bremer, C., Remy, C., and Friday, A. 2024, June. Fake Dashboards Result in Fake Insights: The Challenges of Prototyping Energy Dashboards. Computing Within Limits.
- Bates, O., Remy, C., Cutting, K., Tyler, A., and Friday, A. 2024, June. Exploring post-neoliberal futures for managing commercial heating and cooling through speculative praxis. Computing Within Limits.
- Remy, C., Tyler, A., Smith, P., Bates, O., and Friday, A., 2024, April. Wasted Energy? Illuminating Energy Data with Ontologies. In IEEE Pervasive Computing.
- Cho, H., Maeng, H., Eckley, I.A. and Fearnhead, P., 2023. High-dimensional time series segmentation via factor-adjusted vector autoregressive modeling. Journal of the American Statistical Association
- Bremer, C., Bates, O., Remy, C., Gormally-Sutton, A., Knowles, B. and Friday, A., 2023, April. COVID-19 as an Energy Intervention: Lockdown Insights for HCI. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-7).
- Remy, C. and Gröschel, C., 2023, June. The Role of Technology Towards Net Zero Futures. At re:publica Berlin 2023, the festival for the digital society.
- Mosley, L., Chan, T.-S. and Gibberd, A., 2023, March. The Sparse Dynamic Factor Model: A Regularised Quasi-Maximum Likelihood Approach. arXiv:2303.11892
- Mosley, L., Chan, T.-S. and Gibberd, A., 2023, March. sparseDFM: Estimate Dynamic Factor Models with Sparse Loadings. R package version 1.0.
- Chan, T.-S. and Gibberd, A., 2022, December. Identifying Metering Hierarchies with Distance Correlation and Dominance Constraints. In Proceedings of the 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022 (pp. 1551-1558).