# Deducing Energy Consumer Behavior from Smart Meter Data +
## 5 Experimental Validation
### Outcome Refinement  
In Figure 11 is the disaggregation of a TV from house 10 shown for a one day period. The blue line indicate the FHMM algorithms suggestions on when the TV is ON and OFF. The green line is the actual ON and OFF periods, collected from the sub-meters. As shown in the figure, there is a lot of small spikes that are less than 10 min, and some time is the TV ON for 15 min OFF in 2 and then ON again for another 15 min. This behavior does not fit the normal behavior that is expected from a person. The norm is to see TV more than 20 min at the time, and when a TV is turned OFF it is common that it is turned OFF for at least 15 min before turned ON again. These numbers are a loose estimate, found by observing the sub-meter data. The values used in the _purge and merge process_ is found experimentally. These parameters gave the best results for the TV's in the experiments. In a real deployment scenario, where a _norm filter_ was applied, the coefficients must be selected to fit the normal behavior of the residents. The downside of this approach is that it assumes that the devices are used in a certain manner. For our example of values found for a TV will all watch sessions shorter than 25 min be missed. In Figure 12 is the same signal as in Figure 11 filtered by using the _norm filter_. As seen in Figure 12 does the signal inferred by the filtered FHMM algorithm match more closely to the actual signal. In this example is the norm knowledge applied as a filter, which can filter the disaggregation result. Another approach could be to integrate the norm knowledge as a component in the state transition probability of the HMM. Figure 11: Disaggregation of TV 1 from house 10. Figure 10: F1 score of the load disaggregation methods. Using the specific models, the disaggregation of the main meter signal can be achieved. The statistical disaggregation models selected in this case are found using FHMM algorithm. As discussed in Section 4.4, the disaggregation alone gives a very error-prone signal. The signal is therefore fed through a _norm filter_. As discussed in Section 4.4, the information about how a user normally would use the appliance can be used to filter the disaggregated data to obtain better results. The experiment is conducted with four weeks of data been used to train TV specific models for each house. The analysis for the TV station is conducted over a period of six weeks that is not overlapping with the initial four training weeks. Please notice, the _norm filter_ is only recommended if the accuracy and F1 score of the disaggregation is low. It is undesirable to force the system to make the normal usage as the truth. This can filter away accidental events that lay outside the norm. However, for systems that have a low accuracy and F1 score can this approach help clean the signal. Results  
The viewing habits of the small town obtained by the disaggregation analysis is compared with the actual viewing habit obtained from the sub-meter data. Figure 13 shows the results for the first week. Plots showing the disaggregation of the entire period for each appliance can be found in [49]. The figure shows the viewership, that tells how many in the neighborhood is watching TV at a given time. The green line is the actual viewership, where the blue is the viewership reported by the utility company. The results differed, as one would expect, since the disaggregation process in an environment with high _background consumption_ and high _complexity_ is a hard task. The general trend that many are watching TV in the evening, and not that many in the morning, is represented nicely. This illustrates that the general trend of the viewership is maintained in the disaggregated signal, and this is commonly what is important for this kind of consumer statistics. Figure 12: Purged and merged signal from a TV. Figure 13: Viewership in week one. In Table 1 is the average number of viewers in 3 h timeslots shown. The shown results in the table are the average of all 6 weeks, split up in the different weekdays.