Publications

Household Energy Dataset

• Goddard N., Kilgour, J., Pullinger, M. et al (2021). IDEAL Household Energy Dataset. Edinburgh DataShare. doi.org/10.7488/ds/2836
• Pullinger, M., Kilgour, J., Goddard, N. et al (2021). The IDEAL household energy dataset, electricity, gas, contextual sensor data and survey data for 255 UK homes. Sci Data 8, 146. doi.org/10.1038/s41597-021-00921-y

Energy demand and smart meter policy and practice

Peer-reviewed

• Pullinger, M., Berliner, N., Goddard, N. & Shipworth, D. (2022). Domestic heating behaviour and room temperatures: Empirical evidence from Scottish homes. Energy and Buildings 254, 111509. doi.org/10.1016/j.enbuild.2021.111509.
  • (Open access preprint of the pre-review draft also available at OSF Preprints: doi.org/10.31219/osf.io/jdtx6).
• Lovell, H., Pullinger, M. & Webb, J. (2017). How do meters mediate? Energy meters, boundary objects and household transitions in Australia and the United Kingdom. Energy Research and Social Science 34: 252-259. doi.org/10.1016/j.erss.2017.07.001
• ​Pullinger, M., Lovell, H., & Webb, J. (2014). Influencing household energy practices: a critical review of UK smart metering standards and commercial feedback devices. Technology Analysis and Strategic Management, 26(10). doi.org/10.1080/09537325.2014.977245
• Goddard N., Moore J., Sutton C., Webb J. & Lovell H. (2012). Machine Learning and multimedia content generation for energy demand reduction. 2012 Sustainable Internet and ICT for Sustainability (SustainIT). Full text.

Other publications and presentations

• Department for Energy Security and Net Zero, Pullinger, M. & Kilgour, J. (2023). Smart Energy Savings Competition (SENS): Energy Saver app: Trial-Level Evaluation Report. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1159807/sens-gengame-evaluation.pdf
• Pullinger, M. & J. Kilgour (2023). Energy demand from Scottish homes: Evidence from smart meter data for policymakers and practitioners. Interim report from the Smart Energy Research Lab. Sustain Lab Research Brief. Full text on OSF: doi.org/10.31219/osf.io/b8wu9
• Pullinger, M., Lovell, H. & Webb, J. (2018). Household energy meters: From humble measuring device to instrument of energy system transition? Sustain Lab Research Brief. Full text.
• Pullinger, M. (2014). Smart meters and the household economy of energy: New frontiers for behaviour change policy and technology. International Society for Ecological Economics conference 2014: Wellbeing and equity within planetary boundaries. Rekjavik, Iceland.​​
• Lovell, H., & Pullinger, M. (2013). New economies of residential energy demand reduction. RGS-IBG Annual Conference 2013. London, UK.

Feedback design and effects

Conference and workshop presentations

• Yang, Z., Goddard, N., Webb, L. & Chenn, H. (2021). Electrical Appliance Usage Timeline Data Visualization: Machinima and Dynamic Circle. In e-Energy ’21: Proceedings of the Twelfth ACM International Conference on Future Energy Systems.  DOI: 10.1145/3447555.3466637
• Pullinger, M., Webb, L., Morgan, E. & Webb, J. (2018). Impacts of digital feedback on the precursors of behaviour change: findings from a large experimental field study. BEHAVE 2018, 5th European Conference on Behaviour and Energy Efficiency, Zurich. 6 September 2018. Extended abstract.
• Pullinger, M. (2018). Applying digital methods to understanding occupant behaviour and energy demand. TEDDINET closing workshop, London. 15 June 2018

Research methods

Conference presentations

• Pullinger, M., Goddard, N., & Webb, J. (2016). An experimental research design for evaluating energy feedback. BEHAVE 2016, 4th European Conference on Behaviour and Energy Efficiency . Coimbra, Portugal.​​ Extended abstract

Machine Learning methods

Peer-reviewed

• Berliner, N., Pullinger, M. & Goddard, N. (2021) Inferring room-level use of domestic space heating from room temperature and humidity measurements using a deep, dilated convolutional network, In: MethodsX. doi.org/10.1016/j.mex.2021.101367
• Brewitt, C. & Goddard, N. (2018) Non-Intrusive Load Monitoring with Fully Convolutional Networks. https://arxiv.org/abs/1812.03915v1
• Zhang, C., Zhong, M., Wang, Z., Goddard, N., & Sutton, C. (2018). Sequence-to-point learning with neural networks for nonintrusive load monitoring. The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) (pp. 2604–2611).  http://arxiv.org/abs/1612.09106 ​
• Zhong, M., Goddard, N., & Sutton, C. (2015). Latent Bayesian melding for integrating individual and population models. Advances in Neural Information Processing Systems 28 (NIPS 2015) (pp. 3617–3625). Montreal, Quebec, Canada. http://papers.nips.cc/paper/5756-latent-bayesian-melding-for-integrating-individual-and-population-models.pdf
• Zhong, M., Goddard, N., & Sutton, C. (2014). Signal aggregate constraints in Additive Factorial HMMs, with application to energy disaggregation. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 27 (NIPS 2014) (pp. 3590–3598). Montreal, Quebec, Canada. http://papers.nips.cc/paper/5526-signal-aggregate-constraints-in-additive-factorial-hmms-with-application-to-energy-disaggregation.pdf
• Zhong, M., Goddard, N., & Sutton, C. (2013). Interleaved Factorial Non-Homogeneous Hidden Markov Models for energy disaggregation. NIPS 2013 Workshop on Machine Learning for Sustainability. Lake Tahoe, Nevada, United States.  http://arxiv.org/abs/1406.7665