## 3 Experiment  
The IoT-oriented smart HEMS with the developed novel hybrid UAC-NFC model was experimentally validated by being deployed in a realistic residential field with uncertainties. The experimental set-up of the deployed smart HEMS, as a benchmark against the NILM in a realistic house environment with uncertainties in Taiwan, is illustrated in Figure 5[6; 22]. Major electrical household appliances classified by the novel hybrid UAC-NFC model operated with similar electrical features. In this experiment, features extracted, normalized, and classified in the NILM were P and reactive power (Q). As there may be multiple electrical appliances with a similar power level, P does not represent a particular appliance. So, the proposed model identifies electrical appliances based on P and Q as the distinctive characteristics. In the HEMS, ZigBee wireless communication technology was used. A standardized MySQLTM Connector/ODBC driver, LabSQL Virtual Instruments, using the ADO (ActiveX Data Objects) object collection, and SQL in LabVIEWTM, were installed and used. For DSM/home energy management addressed, the flow of data stores, signal requests, and remote controls of electrical appliances monitored by the HEMS with the proposed novel hybrid UAC-NFC model are shown in Figure 6. DSM was mainly realized through a heterogeneous network involving ZigBee, Radio Frequency (RF), and Infrared Radiation (IR). Universal Asynchronous Receiver/Transmitter (UART), an RS232 RTU-format protocol, was used in the HEMS. The deployed smart HEMS with the proposed novel hybrid UAC-NFC model was used to identify major electrical household appliances. In the HEMS, MATLAB(r) and NTM LabVIEW software suites installed on an ASUS ZENBOOKTM Prime CoreTM i7 UX31A laptop (ASUSteKe Computer Inc., Taipei, Taiwan) were used to implement the novel hybrid UAC-NFC model. Using MATLAB(r) software script server, MATLAB script in LabVIEWTM calls the MATLAB(r) software to execute scripts. In this experiment, major household appliances identified in Line Branch 1 (L1) of the residential field included an electric rice cooker (\(\sim\) 1.10 kW), a multi-mode electric water boiler (\(\sim\) 0.90 kW), a steamer (\(\sim\) 0.80 kW), and a television (\(\sim\) 0.22 kW). The base load in L1 in the household environment was \(\sim\) 0.55 kW, which includes permanent loads. The power source of the electrical wiring of the house environment in Taiwan is AC110 V/60 Hz. The composite current and voltage signals acquired from the main electrical panel of the house environment were simultaneously and continuously sampled by an NTM 9225 DAQ device. The incoming analog signals on each channel of the DAQ device were conditioned, buffered, and then sampled by a 24-bit Delta-Sigma ADC (Texas Instruments Inc., Dallas, USA). With the purpose of providing an accurate representation of in-band signals while rejecting out-of-band signals, the DAQ device uses a combination of analog and digital filtering. An active current transformer that generates output voltage that is proportional to input current was hooked in L1. Its output voltage was wired to one of the channels of the DAQ device. The AC power source of 110 V/60 Hz was directly wired to one of the channels of the DAQ device. The data rate of the DAQ device was set to 2000 samples/s. The DAQ device passed the digitized signals to the laptop via a USB interface every second for the novel hybrid UAC-NFC model in NILM. The laptop ran the NILM to identify if the household appliance(s) was (were) being operated for not. The centralized HEMS, as a benchmark, continually collected data from each of the individual major household appliances every 20 s by talking to the installed ZigBee-based plug-load smart e-meters. ZigBee is an efficient short-range wireless technology in terms of power consumption and deployment scalability [27].