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.