# Deducing Energy Consumer Behavior from Smart Meter Data +
## 4 Proposed Framework
### Outcome Refinement Service  
Not just the sample rate and error rate are determining factors when designing a NILM application. The environment, in which the application is deployed and trained, is critical for the performance of the system. The environment contains the parameters describing the static conditions of the household which the algorithms are deployed in. An example of an environment parameter can be the number of known devices, the number of unknown devices or the number of simultaneously active devices.  
When an appliance is in an environment with a lot of _background consumption_, some of the _background consumption_ can be interpreted as the appliance. This _appliance interference_ creates a scenario where it seems like the appliance is used more than it actually does. Often does the _appliance interference_ create usage patterns that are in contrast to that one would expect. This make it seem like an appliance is used in an unrealistic manner. By improving the method with the knowledge of how a specific appliance is most commonly used, also called the norm usage, it is possible to filter the signal and improve the performance.  
One approach of applying the norm knowledge to the disaggregation result is to use a _norm filter_. This is a filter that takes the results from the disaggregation step and removes unrealistic behavior. This is achieved by using a method that iteratively purges events from the signal and merges events that are too close to each other. The _purge and merge process_ runs two iterations. First is all events shorter than 10 min are purged from the signal. Next is events separated with less than 15 min of OFF time merged. The purge and merge cycle is repeated one more time where events shorter than 25 min are purged and events closer than 30 min are merged.  
The aim of the methodology is to illustrate the potential of this kind of application for a _service provider_. Therefore is the data analysis conducted in a manner, that illustrates how a _service provider_ could do it. This means that only the main meter information is used in the analysis, and the sub-meter information is only used to validate the results. The goal is for the _service provider_ to disaggregate appliance signatures from the main meter signal, in order to use it in some statistical analysis.  
This process is illustrated in Figure 4. It is assumed that the _service provider_ has access to a very general statistical model of the TV. This model can come from some outside database, or be one that the utility company or _service provider_ developed themselves. The first step is to create a household specific model, which is a specialization of the general model. This could be done with sub-meter data. To make the experiment more realistic, the training of the statistical disaggregation models is done using only the accumulated data from the main meter. This is done using the same method as developed for the Parson algorithm [PERSON] (1999). This method finds periods where an appliance is turned ON and OFF without any other appliances changing states, is detected. These ON/OFF single-event fragments are then mapped to specific appliances using the general models. When enough ON/OFF single-event fragments have been collected, this information can be used to train a more specific model. This can happen in a contentious manner where the specific model is improved as more data is collected.  
The result of this process is an appliance usage pattern, that can be used by the _service provider_ to derive various statistical properties.