Publications
- 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
- 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/
Tools and Outputs
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>.