Load Signature Study—Part II: Disaggregation Framework, Simulation, and Applications

Liang J, Ng SKK, Kendall G, Cheng JWM. Load Signature Study - Part II: Disaggregation Framework, Simulation, and Applications. IEEE Transactions on Power Delivery. 2010;25(2):561-569.

This paper reports an evaluation of the disaggregation methods proposed in part I using the previously described accuracy metrics. The evaluation is based on data created through simulation of a household's aggregate demand. The simulation triggers appliance 'on' and 'off' events given a database of appliance signatures and usage likelihoods. The authors conclude that committee decision mechanisms (CDM) outperform single-feature and single algorithm disaggregation methods, with the Maximum Likelihood Estimation CDM performing the best. The authors also note that CDMs are less sensitive to appliance signature noise and aggregate noise.

The use of simulation to generate data upon which the algorithms are evaluated is very interesting. While a range of datasets can be generated far more easily than real-time monitoring, there are also a number of disadvantages. Primarily, any information based on human behaviour, e.g. appliance usage frequency, is lost in the simulation. In addition, any assumptions upon which the simulation is based can cause unrealistic data to be generated. In this paper, the simulation considers appliances whose signatures consist of an 'on' event, followed by a period of constant consumption and ending with an 'end' event. This 'on'-'off' model clearly fails to capture behaviour of appliances with multiple steady states, slow gradual transitions or continuously varying signatures. Furthermore, the simulation assumes only one appliance switch event will occur between samples. This is also an unrealistic assumption, oversimplifying the required disaggregation methods.

Despite the drawbacks of using simulation to evaluate the accuracy of the proposed disaggregation methods, the conclusions related to CDMs are still strong. CDMs are effective techniques to combine feature extraction and classification methods, will be applicable to most NALM approaches.