Net0i

Outcomes

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

Talks and Workshops

 

 

 

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