Tools and Outputs
Optimise! Remy, C. and Bates, O. 2025
A game, interactive narrative systems, and playful exploration of energy systems and user expectations. Through the short gameplay loop of Optimise! the norms and affordances of the thermostat will warp and change, revealing what goes on beyond the dial. Optimise as a game design seeks to incrementally explore the complexity of optimising (Energy) systems through gameplay. Optimise also acts as sandbox for quickly playing with the parameters of energy control systems and peaking at the system behind sites of interaction.
The game is designed for Web browsers and is currently not compatible with mobile.
Research through Games Design (RtGD)
As a kind of subspace within Research through Design, our Research through Games Design approach helps us explore and create knowledge about systems, through designing, playing, testing, and experiencing invisible subsystems and possible alternative systems. As researchers and practitioners building technological interventions, games let us test and explore how our focusing on techno-solutions limit the ways that we can intervene in complex (energy) systems. Following a Research through (Games) Design approach that builds on Speculative Design, Critical Design, and Futures Studies our RtGDs 1) help surface new knowledge and perspectives on the dominant imaginaries of energy and facilities management, 2) bring a broad range of people into a design research process, and 3) enable the design of speculative experiences for players grounded in our search for emergent and alternative systems.
- NotZero, Bates O., Tyler A., and Smith, M. 2025
NotZero is a rules-light story-creation game where players uncover how businesses used ICTs and other means to enact or resist organisational energy and carbon reduction policies. Play as a Climate Anthropologists, from the future, researching how organisations responded to net zero policies with ICT. - Energy Divination, Bates O. and Smith, M. 2025
Energy Divination is a quick visual prompt game about using only visual information to predict energy futures. It encourages an alternative approach and perspective to the data-centric thinking that happens in energy research and ICT interventions. - Making a Meal out of a Mountain, Bates O. and Kirman, B. 2025
A zine exploring what RtGD can be and how you might use this in your research practice. - A Supervisor’s Quick Guide to Research through Game Design, Kirman B. and Bates, O. 2025
A supervisors guide to RtGD.
Interactive Energy Dashboard, Remy, C. 2024
An interactive dashboard that takes historical energy data and displays it in different formats. Users can compare buildings with each other as well as to established industry benchmarks, search for information about specific buildings or sources of energy data readings, and see times of consumption considered not normal, i.e., particularly high, low, or simply not following expected patterns (so called “anomalies”).
Energy Usage Clustering Dashboard, Granados-Garcia, G., 2024, 10.5281/zenodo.14396956, https://github.com/Cuauhtemoctzin/Energy_Usage_Clustering,
This dashboard benchmarks and clusters the different sources used in the Lancaster University buildings during 2023.
Multivariate Time Series Anomaly Scoring Dashboard, Granados-Garcia, G., 2024, 10.5281/zenodo.14283663, https://github.com/Cuauhtemoctzin/anomaly_tool,
This dashboard allows public users to upload and explore anomalies in their data to benchmark multivariate time series via anomaly scores.
Lancaster University Energy Data (2023).
Dataset of energy data from 2023, in one hour aggregates, of several buildings and sub-meters (1153 data streams), in JSON format.
Software
AnomalyScore R Package, Granados-Garcia, G., 2024, 10.32614/CRAN.package.AnomalyScore https://github.com/Cuauhtemoctzin/AnomalyScore
This Package helps to compute anomaly scores for multivariate time series. The scores are defined based on a K nearest neighbor algorithm using different approaches to determine distances between time series.
anomalous, Smith, P., 2024, 10.5281/zenodo.14234769
https://waternumbers.github.io/anomalous/
an R package for detecting anomalies around profiles,
sparseDFM: Estimate Dynamic Factor Models with Sparse Loadings. Mosley, L., Chan, T.-S. and Gibberd, A., 2023, March. 10.32614/CRAN.package.sparseDFM
Implementation of various estimation methods for dynamic factor models (DFMs) including principal components analysis (PCA) Stock and Watson (2002) <doi:10.1198/016214502388618960>, 2Stage Giannone et al. (2008) <doi:10.1016/j.jmoneco.2008.05.010>, expectation-maximisation (EM) Banbura and Modugno (2014) <doi:10.1002/jae.2306>, and the novel EM-sparse approach for sparse DFMs Mosley et al. (2023) <doi:10.48550/arXiv.2303.11892>.
Publications
- Bates, O, Remy, C., Tyler, A., Smith, M., and Friday, A., 2025. Playful Explorations: Non-domestic energy research through games design, ICT4S 2025 Posters and Demos
- Remy, C., Bates, O., and Friday, A., 2025 ContextViz: Making Context and Systems Knowledge Visible in Non-Domestic Energy Demand Dashboards. ICT4S 2025 Posters and Demos
- Bremer, C., Knowles, B. and Friday, A., 2025. Of Ironies and Agency: Energy Professionals’ Views on Digital Interventions and Their Users. CHI Conference on Human Factors in Computing Systems.
- Granados-Garcia, G., Eckley, I., Electricity Demand of Buildings Benchmarked via Regression Trees on Nearest Neighbors Anomaly Scores, 2024 (In progress)
- 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
- 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).
Talks and Workshops
- Conversations with Complexity: Making a Meal out of a Mountain Oliver Bates and Ben Kirman, 20 May 2025, BDiGRA 2025
- Rebound Archetype Cards workshop, Laetitia Bornes, Marcia Tavares Smith and Oliver Bates, 14 May 2025, Towards sustainable digital futures, a two-day symposium
- “Transforming commercial energy demand through data science” Adrian Friday, Christian Remy, Christina Bremer and Oliver Bates. Energy Systems Catapult – Value in energy data webinar series on February 5th 2025.
- Rebound GHG Effects in AgriTech. Matthew Broadbent and Oliver Bates. 1st International Workshop on Low Carbon Computing, 2024
https://www.sicsa.ac.uk/wp-content/uploads/2024/11/LOCO2024_paper_6.pdf - Messy Energy Data. Sense-making via change-point and anomaly detection – Paul Smith & Idris Eckley, ENBIS-24, September 2024 https://conferences.enbis.org/event/34/contributions/733/