## 3 Experiment
In the NFC, the Gaussian-type membership function, rather than other types of membership function, was chosen for the following three reasons. First, the total number of free parameters to be trained was minimized because the Gaussian-type membership function only requires two factors to be formulated. Second, the Gaussian-type membership function ensures that the firing strength of each fuzzy rule computed is always non-zero. Third, the Gaussian-type membership function is continuously differentiable, and its derivative is always non-zero. Figure 7: Feature space being of real power (P) and reactive power (Q). There are 12 classes in the feature space, which are marked by different color. The inputs of the NFC in Figure 3 directly convey the extracted and normalized electrical features. During the experiment, feature data were normalized within [0, 1]. The output of the NFC in Figure 3 is an identified class label. Two-fold cross validation was conducted. In the novel hybrid UAC-NFC in case 1, during the UAC process, \(r\), the radius in Table 1, was set to 0.3. \(\alpha\) in Equation (4) is 1. During the GD process, the training rate, \(\eta\), was 0.023; a value for the maximum training iteration of 500 was used. A trained Mean Squared Error (MSE) of 0.004 was achieved, which produced an overall classification rate of 92.92% in training. The overall classification rate in tests was 95.83%, which was obtained with a value of MSE of 0.003 in case 1. In the novel hybrid UAC-NFC in case 2, during the UAC process, \(r\) was set to 0.3. \(\alpha\) in Equation (4) was 1. During the GD process, the training rate \(\eta\) was 0.01; the maximum training iteration value of 500 was used. The trained MSE of 0.006 was achieved, which produced an overall classification rate of 95.00% in training. The overall classification rate in tests was 95.63%, which was obtained with a value of MSE of 0.005 in case 2. Table 3 shows the clustering results obtained using the UAC process conducted in cases 1 and 2. Figure 8 shows the training results obtained in cases 1 and 2. Table 4 summarizes the load classification results obtained by the novel hybrid UAC-NFC model proposed in this article and evaluated in this experiment. A cross-validation procedure [21] was applied to the feature data, where the entire feature dataset was split into a training dataset and a test dataset in this experiment. The proposed and evaluated novel hybrid UAC-NFC model for load classification in DSM produced an average and generalized overall classification rate [29] of 95.73%. Different types of electrical household appliances were identified by the novel hybrid UAC-NFC model for DSM/home energy management. This work is a revision of the original work that was proposed in [PERSON] et al. [6]. A comparison of the optimization methods used to optimize the NFC was presented in the same article [6]. Mainly, this article focused on designing and implementing an IoT-oriented smart HEMS based on a novel hybrid UAC-NFC model in NILM for load classification in order to overcome the difficulties in distinguishing electrical appliances operated under similar electrical features and classified in an unsupervised and self-organized sense.