NIALM papers at IECON 2012

The 38th Annual Conference of the IEEE Industrial Electronics Society was held in Montréal, Canada, a few months ago and I've only just had the chance to go through the proceedings. There were quite a few papers related to NIALM, so I thought I'd list, link and summarise them here:

• Anderson, J., Sadhanala, A., & Cox, R. (2012). Using smart meters for load monitoring and active power-factor correction. IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society (pp. 4872–4876)
• This paper describes a novel application for how NIALM can be used to detect reactive loads for active power-factor correction. The proposed approach uses a NIALM system to detect large reactive loads (e.g. air conditioning unit), and issue a control signal to the active power filter. The filter compensates for the load by reducing the reactive power to 0, therefore correcting the aggregate power factor. The NIALM approach utilises steady and transient-state features which extracted from the real power, reactive power and spectral envelopes, which in turn are calculated from high frequency measurements of aggregate current and voltage.
• Anderson, K. D., Berges, M. E., Ocneanu, A., Benitez, D., & Moura, J. M. F. (2012). Event detection for Non Intrusive load monitoring. IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society (pp. 3312–3317)
• This paper focuses on the problem of event detection; detecting the times at which one appliance (or more) has turned on or off. The authors start by summarising state-of-the-art event detection algorithms and metrics. They then give a comparison of the performance of an event detection method based on the generalised likelihood ratio with its parameters optimised by a number of different metrics. The authors conclude that a metric based on the total power changes yields the best performance, and attribute its high performance to its weighting of the important of appliances by their energy consumption.
• Bier, T., Abdeslam, D. O., Merckle, J., & Benyoucef, D. (2012). Smart meter systems detection & classification using artificial neural networks. IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society (pp. 3324–3329)
• This paper describes a method for disaggregating the refrigerator from a household's aggregate load using artificial neural networks. The authors use a similar training method to my AAAI paper, in which the refrigerator model is trained during the overnight period. To detect appliance switch events, the authors adopt the method proposed by Hart with slightly tuned parameters. The performance of the approach is demonstrated using 50 days of data collected from a single household, and the accuracy of turn-on classifications is compared to the method proposed by Hart.
• Du, L., Yang, Y., He, D., Harley, R. G., Habetler, T. G., & Lu, B. (2012). Support vector machine based methods for non-intrusive identification of miscellaneous electric loads. IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society (pp. 4866–4871)
• The contribution of this paper is to the problem of appliance identification (classify the type of a  single appliance given its power demand) as opposed to disaggregation (determine the power demand of each appliance type given the aggregate power demand). The authors collect high frequency samples (kHz) of each load's current and voltage, from which a number of features corresponding to electrical characteristics are extracted. The authors propose the use of a hybrid supervised self-organising map and support vector machine approach to classify loads, and show that the hybrid outperforms both approaches individually.
• Sinha, M., Desai, B., & Cox, R. (2012). Using smart meters for diagnostics and model-based control in thermal comfort systems. IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society (pp. 3600–3605)
• The authors contribute a method for the thermal modelling of buildings using a combination of NIALM and additional sensor measurements. Such modelling allows both HVAC problem diagnosis and the formulation of schedules for HVAC systems with variable speed compressors. The approach learns the thermal properties of a building by using the HVAC operating schedule (as determined by the NIALM from smart meter data), the external temperature, the internal temperature and solar irradiance (as measured by individual sensors). The authors test their diagnostic system by removing some coolant from the HVAC system and observing the change in performance, and also show the performance of their predictive control model for variable speed HVAC systems.

2 Papers accepted at AAMAS and IUI on AgentSwitch

As part of the ORCHID project, we recently had two papers accepted at international conferences based on AgentSwitch; a domestic energy recommendation platform. AgentSwitch utilises electricity usage data collected from households over a period of time, to realise a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. I've been responsible for the load detection module of AgentSwitch over the past few months, and am looking forward to improving it over the final year of my PhD. The following two papers provide two different perspectives on the project. The first gives the algorithmic detail and accuracy evaluations of the individual system modules. The second describes a user evaluation of AgentSwitch, which reveals the strengths and weaknesses of the system as an energy-related recommender system.

• Sarvapali Ramchurn, Michael Osborne, Oliver Parson, Sasan Maleki, Talal Rahwan, Trung Dong Huynh, Steve Reece, Muddasser Alam, Joel Fischer, Greg Hines, Enrico Costanza, Luc Moreau, Tom Rodden. AgentSwitch: Towards Smart Energy Tariff Selection. In: 12th International Conference on Autonomous Agents and Multi-Agent Systems. Saint Paul, Minnesota, USA. 2013.

• Joel Fischer, Sarvapali Ramchurn, Michael Osborne, Oliver Parson, Trung Dong Huynh, Muddasser Alam, Nadia Pantidi, Stuart Moran, Kaled Bachour, Steve Reece, Enrico Costanza, Tom Rodden, Nicholas Jennings. Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling. In: International Conference on Intelligent User Interfaces. Santa Monica, CA, USA. 2013.

It seems that papers follow the same rule as British buses; you wait ages for one and then two arrive at the same time.

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.