Research briefs

• Pullinger, M., Lovell, H. & Webb, J. (2018). Household energy meters: From humble measuring device to instrument of energy system transition?

Publications and conference presentations

Energy demand and smart meter policy and practice

• 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
• ​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). http://doi.org/10.1080/09537325.2014.977245 
• Pullinger, M. (2014). Smart meters and the household economy of energy: New frontiers for behaviour change policy and technology. In 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. In RGS-IBG Annual Conference 2013. London, UK. 
• Goddard N., Moore J., Sutton C., Webb J. & Lovell H. (2012). Machine Learning and Multimedia Content Generation for Energy Demand Reduction ​

Feedback design and effects

• 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. 6 September 2018. In BEHAVE 2018, 5th European Conference on Behaviour and Energy Efficiency, Zurich. Extended abstract.
• Pullinger, M. (2018). Applying digital methods to understanding occupant behaviour and energy demand. TEDDINET closing workshop, London. 15 June 2018

Research methods

• Pullinger, M., Goddard, N., & Webb, J. (2016). An Experimental Research Design for Evaluating Energy Feedback. In 4th European Conference on Behaviour and Energy Efficiency (Behave 2016) (pp. 8–9). Coimbra, Portugal.​​

Machine Learning methods

• 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. In 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. In 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. In NIPS 2013 Workshop on Machine Learning for Sustainability. Lake Tahoe, Nevada, United States.  http://arxiv.org/abs/1406.7665