## 3 Experiment
ZigBee Alliance [28] was formed in 1998 by Honeywell Corporation (Honeywell International Inc., Morris Plains, USA), whose aimed to use IEEE 802.15.4 low-power wireless network protocols as the basis for the development of the specification of IoT applications. The ZigBee protocol is a low-power wireless transmission protocol, providing a suitable data rate for control and monitoring purposes of smart houses as a use case [27]. So, the ZigBee-based wireless communication network was used in the residential environment for the HEMS presented in this article. In this experiment, the total number \(N_{app}\) of electrical household appliances powered by L1 of the electrical wiring in the residential environment and monitored by the smart HEMS, having the proposed novel hybrid UAC-NFC model, was four. In total, 16 (\(2^{Napp}\)) operational combinations/classes had to be classified. The household appliances monitored in the residential environment included inductive and resistive loads. The load operation scenarios classified included the scenarios where household appliances are simultaneously energized or de-energized. In this experiment, 4 load operation scenarios were excluded, since, in reality, the power-intensive household appliances should not be used at the same time as the conductor would overload. The centralized HEMS communicating with the plug-load smart e-meters via a ZigBee-based wireless communication network identified the monitored household appliances intrusively. The NILM using the novel hybrid UAC-NFC model to analyze the composite voltage and current signals, acquired at the main electrical panel in the residential environment, classified the household appliances non-intrusively. In [PERSON] et al. [22], only one electrical feature, P, was considered for load classification. In [PERSON] et al. [6],  
Figure 5: Experimental set-up of the IoT-oriented smart HEMS utilizing the proposed novel hybrid UAC-NFC model in a realistic residential environment. Figure 6: Flow of data stores, signal requests, and controls of electrical appliances monitored in the HEMS. a mechanism that systematically constructs an NFC by grouping input-output data pairs into clusters to represent fuzzy if-then rules in an unsupervised and self-organized manner was not developed. In this article, a novel hybrid UAC-NFC model is developed and evaluated using this experiment. In this experiment, on-site 80 voltage and current measurements were recorded off-line for each load operation scenario: 40 randomly-chosen voltage and current measurements were used for training, while the remaining 40 voltage and current measurements were used for tests. A total of 960 data pairs were collected. Figure 7 illustrates the feature space of P (Watts) and Q (Var) in this experiment. P and Q stand for real power and reactive power measured, respectively, which were extracted from the electrical household appliances and used as the electrical features/input feature variables for the NILM as a load classification task for the NFC. There are 12 classes in Figure 7, which are addressed and listed in Table 2. Observation of feature data in Figure 7 shows that high ambiguity existed in the data. The ambiguity was caused by uncertainties where household appliances were identified under similar P and Q. Thus, NFC was conducted. As illustrated in Figure 7, the 12-class load classification problem can be de-composed into 3 classification problems following a divide-and-conquer process. The proposed hybrid UAC-NFC model proposed was applied to cases 1 and 2. In this experiment, the UAC process, the nearest-neighborhood clustering in Section 2.2, was first applied to feature data in Figure 7. Following the UAC process used to automatically construct the NFC of Equation (2) without supervision, the GD process fine tunes the free parameters of the constructed NFC of Equation (2). The GD process trains the constructed NFC. The NFC, shown in Figure 3 and trained through the GD process, included three network layers, and its training rule was based on Equations (5)-(7).