## 1 Introduction
In these three studies [8; 9; 10], DWT, instead of FFT, was conducted and used to detect and analyze transient response of electrical appliances as feature extraction for NILM. [PERSON] et al. [11; 12] proposed a hybrid classification strategy that combines the conventional Particle Swarm Optimization (PSO) with Back-Propagation-Artificial Neural Networks (BP-ANNs) for NILM. The conventional PSO optimizes the weight coefficients of the BP-ANNs, with the purpose of improving the classification accuracy of load classification. The strategy proposed in [PERSON] and [PERSON] et al. [10; 11] was verified by Electromagnetic Transients Program simulations and field measurements. The results reported indicate the proposed strategy significantly improves the recognition accuracy and computational efficiency of the BP-ANNs under multiple load operation conditions. The k-NN and BP-ANNs were used as load classifiers to classify electrical appliances monitored [7; 8; 9; 11; 12]. However, k-NN and BP-ANNs cannot handle uncertainties in electrical appliances or operational combinations of electrical appliances monitored and classified with similar electrical characteristics, resulting in ambiguities on feature data. Advanced machine learning has been applied in NILM. In [PERSON] et al. [13], a new Markov-style probabilistic framework for NILM was proposed, wherein a modified Viterbi algorithm that uses approximate semi-Markov models to retain a robust computationally-efficient solution to pristinely infer draws of real power (P) of monitored electrical household appliances, was applied and evaluatedin a residence. The main advantage of the modified Viterbi algorithm proposed in [PERSON] et al. [13] is that the complexity of the NILM is linearly proportional to the number of household appliances monitored. However, the complexity of the NILM by other Markov-style probabilistic algorithms, which model household appliances by a factorial Hidden Markov Model [14; 15; 16], exponentially grows with the number of household appliances monitored [17]. The Markov-style probabilistic algorithms [13; 14; 15; 16] experience the same problem in which they are not capable of addressing household appliances with similar P [17]. Ambiguities from similar P exist in feature data. The Graph Shift Quadratic Form constrained Active Power Disaggregation algorithm [18] is also unable to address household appliances that are identical in P. In addition to P, where there may be multiple electrical appliances with a similar power level, power patterns, such as characteristics retrieved from household appliances, were considered for NILM and identified by dynamic time warping in [PERSON] et al. [19]. The NILM techniques developed in the past and surveyed above have improved feature extraction with developed classification algorithms; however, difficulties still remain in distinguishing electrical appliances operated under similar electrical features and classified as unsupervised and self-organized. Electrical appliances with similar electrical features are difficult to discern. This article focuses on designing and implementing an IoT-oriented smart HEMS based on a novel hybrid Unsupervised Automatic Clustering-integrated Neuro-Fuzzy Classification (UAC-NFC) model in NILM, in order to overcome the difficulties in distinguishing electrical appliances operated under similar electrical features showing ambiguities and classified as unsupervised and self-organized. Fuzziness [6] in fuzzy theory is introduced in the hybrid UAC-NFC model proposed in this article. This work is a revision of the original work by [PERSON] et al. [6]. NILM identifies individual electrical appliances by analyzing composite electrical signals acquired by only one single set of plug-panel voltage and current sensors in a field. The hybrid UAC-NFC model proposed in this article is able to handle uncertainties in which electrical appliances monitored in a realistic experimental household environment are used and classified under similar electrical features.