# Low-Rate Non-Intrusive Load Disaggregation with Graph Shift Quadratic Form Constraint
## 3 Experiment and Results
### Experimental Results and Discussion  
In this paper, the dataset used for the experiment is the REDD dataset, one open-access dataset containing detailed power usage information from US houses and specifically towards the task of energy disaggregation [48], downsampled to 1 min. For validation purposes, we treat the main type of appliance on the individual circuit as known appliance. They are: microwave (MW), dishwasher (DW), kitchen outlet (KO), stove (ST), air-conditioning (AC), washer dryer (WD), oven (OV), bathroom_gfi (BG), electronics (EL), refrigerator (REFR), and lighting (LT). Besides, baseload and unknown appliances are respectively abbreviated as BL and UN. In order to implement a realistic energy disaggregation task and verify the effectiveness of our algorithm, the data selected for the experiment should contain all the household devices. 1. Effectiveness of GSQF-APD algorithm. In the experiment, we use the aggregated active power readings of House 1 and House 2 from REDD dataset, and respectively select a period of time without any invalid intervals to demonstrate the disaggregation performance of the proposed algorithm. The disaggregation result of GSQF-APD for two typical days of House 2 is shown in Figure 2.  
The red dotted curve in Figure 2 stands for the aggregate power, and other curves indicate the estimated power consumption of each appliance, which are separated from the aggregate power through GSQF-APD algorithm. Because the aggregated power curve is the superposition of each appliance power curve; to a certain extent, the power consumption of each independent appliance will be reflected on the aggregate power curve. Note that there is a good alignment between the disaggregated results and the actual aggregate power, which demonstrates the excellent performance of GSQF-APD algorithm as a whole. For a more detailed comparison between the ground truth and the disaggregated appliances' active power, we conducted a further experiment using the data from House 1, which has a similar number of appliances to House 2 but presents more working states, and thus it is more challenging to disaggregate [49]. The result is shown in Figure 3. In order to evaluate the performance on a single or multiple activations, an appropriate time interval is represented in the figure for each appliance. As shown in Figure 3, the appliances with high steady powers are relatively easy to disaggregate, while those appliances with multiple working states or complex operation period are difficult to recognize. For example, the washer dryer, the oven, the bathroom_gfi, and the kitchen outlet have been successfully reconstructed, however, the microwave and the dishwasher are reconstructed with some errors. For microwave, it performs well in the activation period, whereas it brings in some false energy distribution during the off period. For dishwasher, owing to the multiple power levels, there are some working states not detected or recognized erroneously. Figure 3: Detailed disaggregation results for House 1: the disaggregated power (D) is compared against the ground truth (GT) signals. Figure 2: Disaggregation result in House 2 for two days. Comparison of two GSP-based methods, GSQF-APD and GLQF-APD [40]. In this experiment, we compare the two approaches in two sections. In the first section, we compare the classification performance for House 2 of the two methods. The corresponding results are reported in Figure 4, where the precision, recall, and \(F\)-Measure are reported in Figure 4a-c, respectively. The values are related to Table 1, which is very informative about details of each appliance's classification result and the absolute improvement of three classification metrics. Firstly, contrasting Step 1 and Step 2 of the GSQF-APD algorithm, we can see that the Step 2 performs better than Step 1 in classification accuracy, where the precision, recall, and \(F\)-measure of most appliances are significantly improved.