## 1 Introduction  
Owing to global warming and climate change, monitoring and managing residential, commercial, and industrial major electrical appliances is of vital importance. Major appliances are controlled and used to respond to Demand Response (DR) schemes from power utilities, so that electrical energy efficiency related to energy demand from downstream sectors in a smart grid can be improved and greenhouse gases produced from carbon pollution can be reduced. DR schemes offer financial incentives to take actions to reduce or shift loads in correspondence to market price behavior. DR plays a crucial role in efficiently capturing the benefits of Demand-Side Management (DSM) to ensure demand flexibility including peak load reduction [1]. To track electrical energy efficiency of individual electrical appliances that are monitored and managed in fields of interest, one way is to use Internet-of-Things (IoT)-oriented Energy Management Systems (EMS), where energy Information and Communication Technology (ICT) is applied for DSM. In an IoT-oriented EMS deployed in a field of interest, plug-load smart e-meters, called smart sockets, are installed for individual electrical appliances and are wirelessly networked with a centralized gateway. In a smart home connected to a smart grid via Advanced Metering Infrastructure (AMI), the IoT-oriented Home EMS (HEMS), which identifies power consumption on individual major household appliances by many smart e-meters, monitors each individual major household appliance intrusively. However, conducting HEMS burdens consumers with a large construction investment and annual maintenance costs [2]. In contrast with HEMS conducted for DSM/home energy management, Non-Intrusive Load Monitoring (NILM) analyzes composite electrical signals acquired from only one minimal set of plug-panel voltage and current sensors and then provides appliance-level energy usage information and monitors each individual major household appliance non-intrusively. NILM conducted in a smart home with an IoT-oriented HEMS, considered part of DSM in a smart grid, is a cost-effective technique [3] for extracting appliance-level energy consumption information by monitoring and analyzing aggregated signals acquired at the entrance of electric power from a utility. NILM has been examined and applied in many previous studies [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. In [PERSON] et al. [3], NILM was treated as a linear integer-programming problem, and was solved by an iterative clustering algorithm. [PERSON] et al. [4] presented a novel hybrid classification technique that combines the supervised self-organizing feature map with Bayesian classification for NILM. The diversity and similarity of different types of electrical household appliances operated and classified under similar steady-state electrical features were considered in [PERSON] et al. [4]. However, the novel hybrid classification technique in [PERSON] et al. [4] cannot classify household appliances with multi-state transitions. The classification of electrical household appliances addressed in [PERSON] et al. [4] requires an in-depth investigation on operation principles of household appliances [5]. To classify electrical household appliances with multi-state transitions under similar electrical features, [PERSON] et al. [6] proposed an NILM as load classification using fuzzy logic theory considering uncertainties. A mechanism that can automatically and systematically construct fuzzy classifiers in a nearest neighborhood manner was not developed for NILM in [PERSON] et al. [6]. In [PERSON] et al. [7], the k-nearest neighbors (k-NNR) method, producing an error rate that ranged from 20% to 29% was used to recognize residential appliances. In other prior studies [8; 9; 10], a Discrete Wavelet Transform (DWT)-based transient feature extraction method for NILM was proposed. DWT allows simultaneous time and frequency localization, whereas time localization by Fast Fourier Transform (FFT) is not possible.