Top papers of 2012 for Non-Intrusive Appliance Load Monitoring (NIALM)

A little over a year ago I posted a list of my top 10 papers on non-intrusive appliance load monitoring. It quickly became my blog's most popular post, and also inspired more comments than any other post. Collecting and summarising academic literature is clearly useful to the community, and as a result I decided to collect the papers published during 2012 that I found most useful. There were many more papers published than I have time to describe here, but a more comprehensive list can be found in my on-line Mendeley reference library. I hope you find this list useful, and as always feel free to leave a comment!

• Armel, K. C., Gupta, A., Shrimali, G., & Albert, A. (2013). Is disaggregation the holy grail of energy efficiency? The case of electricity. Energy Policy, 52, 213–234.
• The authors provide an argument for applying disaggregation algorithms to smart meter data. They focus on many practical problems which are often ignored in academia, such as data availability, transmission capabilities and deployment costs. The authors conclude with a set of recommendations for both disaggregation algorithms and smart meter deployments, which if followed will aid the deployment of such technology at national scales.
• Kolter, J. Z., & Jaakkola, T. (2012). Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation. International Conference on Artificial Intelligence and Statistics (pp. 1472–1482). La Palma, Canary Islands.
• This paper describes an algorithm for efficiently disaggregating appliances by modelling the problem as a factorial hidden Markov model. In such a model, sudden increases or decreases in meter measurements are used to identify appliances turning on or off. The authors extend the model to include an additional component which means that not all appliances within the household are required to be modelled. The proposed model and inference algorithm are evaluated using both simulated data and the REDD data set.
• Parson, O., Ghosh, S., Weal, M., & Rogers, A. (2012). Non-intrusive load monitoring using prior models of general appliance types. Twenty-Sixth Conference on Artificial Intelligence (AAAI-12).
• This paper contributes an unsupervised training method for NIALM systems. The approach uses prior appliance models which describe generalisable appliance behaviour (e.g. behaviour of all refrigerators), which are tuned to match a specific appliance instance (e.g. one particular refrigerator) using only aggregate data. The approach is benchmarked against two different training methods: a variant in which the prior models are not tuned, and a variant in which the prior models are tuned using sub-metered data.
• Reinhardt, A., Baumann, P., Burgstahler, D., Hollick, M., Chonov, H., Werner, M., & Steinmetz, R. (2012). On the Accuracy of Appliance Identification Based on Distributed Load Metering Data. 2nd IFIP Conference on Sustainable Internet and ICT for Sustainability (SustainIT).
• The core contribution of this paper is an approach to solving the appliance identification problem. However, I've included it here since I believe the tracebase data set released with this paper is highly relevant to the NIALM community. To the best of my knowledge, the data set contains the largest public collection of appliance power data, as described in my previous blog post. As a result, it provides the potential for households to be simulated by summing arbitrary combinations of actual appliance loads to produce artificial aggregate loads.
• Wang, Y., Hao, X., Song, L., Wu, C., Wang, Y., Hu, C., & Yu, L. (2012). Tracking states of massive electrical appliances by lightweight metering and sequence decoding. Proceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data (pp. 34–42). New York, NY, USA.
• The authors of this paper address two problems in NIALM. First, they present an algorithm to perform efficient inference in factorial hidden Markov models for appliance disaggregation by forgetting unlikely state transitions. Second, they present an approach to determine the number and positions of additional circuit-level meters so as to ensure a minimum accuracy of disaggregation. They evaluate their approaches using both simulated data and data from Stanford's Powernet data set.
• Zoha, A., Gluhak, A., Imran, M. A., & Rajasegarar, S. (2012). Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey. Sensors, 12, 16838–16866.
• This overview paper gives a description of the current state of the art in academia. In addition, a list of accuracy metrics and publicly available data sets is given. The authors highlight some limitations of the field, such as the invasiveness of manual training processes. They conclude with a set of directions to advance the field, including a suggestion to replace the manual construction of appliance databases with unsupervised training methods.