## 1 Introduction
### IoT-Oriented Smart HEMS  
Figure 1 shows a block diagram of the centralized IoT-oriented smart HEMS [6] with the proposed novel hybrid UAC-NFC model applied for NILM. The HEMS conducted in a house environment is an ARM(r) (Advanced RISC Machine) CortexTM-A9-based embedded system (Texas Instruments Inc., Dallas, USA), which is configured with an LAMP (LinuxTM Operating Systems ([PERSON], Helsinki, Suomi) + Apache HTTP server (Apache Software Foundation, Forest Hill, MD, USA) +  
MySQLTM relational database (Oracle Corporation, Redwood City, CA, USA) + server-side PHP (PHP: Hypertext Preprocessor) scripting language ([PERSON], Qeeterarusuaq/Disko Island, Greenland)) development stack [6]. The HEMS--Home Gateway (HG)--serves as a central home controller connecting the house with a utility for DSM/home energy management via AMI in a smart grid. The centralized HEMS is able to continually determine the power consumption for each major household appliance monitored and networked together through a ZigBee (Zigbee Alliance, Davis, USA)-based wireless communication network. I chose to use the ZigBee-based plug-load smart e-meters in this article to evaluate the recognition rate of the proposed novel hybrid UAC-NFC model applied for NILM. The model applied for NILM was implemented on a laptop computer in the LabVIEWTM (National Instruments, Austin, TX, USA) environment with MATLAB(r) (The MathWorks, Inc., Natick, MA, USA). As shown in Figure 1, the ZigBee wireless communication technology, an ICT, was conducted in the HEMS. A standardized MySQLTM (Oracle Corporation, Redwood City, USA) Connector/Open Database Connectivity (ODBC) driver, LabSQL (Jeffrey Travis Studios LLC, Austin, TX, USA) Virtual Instruments using the ActiveX (Microsoft, Redmond, USA) Data Objects (ADO) for object collection, and Structured Query Language (SQL) in LabVIEWTM were installed, conducted, and used. Due to the ZigBee wireless communication network, the HG communicating with the household appliances was able to remotely direct/distribute load control considering DSM/home energy management. An enabled remote access MySQLTM relational database was configured on the HG. Common Gateway Interface (CGI) programs were coded. The HyperText Transfer Protocol (HTTP) server can interact dynamically with homeowners. An HTTP server-based user interface, showing detailed electrical energy information identified by the HEMS utilizing the novel hybrid UAC-NFC model, was designed. Figure 2 shows the workflow of a typical NILM [6], applied in this article and deployed on the centralized HEMS and studied against the centralized HEMS as a benchmark. In an NILM using the novel hybrid UAC-NFC model proposed in this article, both the raw composite analog current and voltage signals are simultaneously and continuously acquired by a National Instruments (NI)TM Data Acquisition (DAQ) device (National Instruments, Austin, TX, USA) with low-pass filters and Analog-to-Digital Converters (ADC). The DAQ device was installed at the main electrical panel of a realistic house field. The raw composite analog voltage and current signals sensed by one single minimal set of voltage and current sensors are filtered through low-pass filtering. Then, the signals without high-frequency noisy signals removed are digitalized via analog-to-digital conversion. Once the digitized composite signals are analyzable, the NILM executes (1) feature  
Figure 1: Block diagram of the centralized Internet of Things (IoT)-oriented smart Home Energy Management System (HEMS) with the proposed novel hybrid Unsupervised Automatic Clustering-integrated Neuro-Fuzzy Classification (UAC-NFC) model applied for Non-Intrusive Load Monitoring (NILM). extraction by steady-state analysis and (2) load classification using the novel hybrid UAC-NFC model applied to extracted and normalized electrical feature data. The NILM in Figure 2 was implemented on a laptop computer wired with the NITM DAQ device (National Instruments, Austin, TX, USA) via a USB interface.