## 1 Introduction  
With the development of science and technology, the electricity consumption is constantly growing, which stimulate the development of Smart Grid [1], a new efficient, reliable, flexible and optimized electric power system [2]. Smart Grid requires that smart meters provide electricity consumption information to help in making demand management strategies [3]. Research has shown that the total electricity information feedback can produce energy saving by approximately 15% [4]. Furthermore, if detailed electricity consumption information is provided, the energy sector can adjust hourly dynamic energy price to reduce the demand in peak hour [5], and inform consumers about high energy consumable appliance, there will be more benefits than energy savings [6]. Traditional intrusive load monitoring technique requires installing sensors on each appliance for monitoring the load electricity consumption details. Although accurate monitoring can be achieved, it also causes high hardware installation cost [7, 8]. Non-intrusive load monitoring (NILM), as proposed in 1992 [9], is a novel and cost-effective technique. NILM, using pattern recognition technologies and machine learning methods, decomposes the total energy consumption measured at the household level by a single point sensor into the energy consumption of individual appliances [9]. Thus, NILM has the advantages of low cost and easy implementation. In recent years, several NILM approaches have been proposed [10]. [PERSON] proposed event-based method to identify appliances [9]. Literature [11] adopts Naive Bayes classifier to identify the multiple appliances operating. Wavelet transform are used to identify the appliances by analyzing the power spectrum [12]. Yet, a number of problems emerged in recent works. At first, high-frequency feature leads to higher identification accuracy. However, high-frequency feature extraction needs additional measuring equipment and also causes high computational complexity [13]. Large-scale applications of NILM demands that appliances in residential or buildings could be directly monitored without adding additional costs. Load disaggregation utilizingsmart meter reading is easy to implement in real life. Second, simultaneously operating appliances not only increases the complexity of identifying appliance-specific consumption patterns [14; 15], but also the consequently overlapping operation makes it difficult for event detection [16]. Finally, the accuracy metrics of NILM approach are difficult to be defined consistently [14; 15; 17]. Many approaches focus on the accuracy of the on/off detection [18; 19; 20], and only few studies consider the retrieving appliances consumption information. To solve the above problems, we proposed an iterative load disaggregation approach based on appliance consumption pattern (ILDACP), which will be implemented in a reduced invasiveness way. Proposed approach combines the fuzzy C-means clustering algorithm (FCM) and Dynamic Time Warping (DTW) to identify the on/off states of each appliance in sequence and sub-sequence searching DTW (SSDTW) to accurately disaggregate total power into single-level base on the different consumption pattern of each appliance. In our work, the consumption pattern of each appliance is described in low sampling, as high sampling leads to higher hardware cost [21]. FCM algorithm clusters the total consumption, high power level values of which will be clustered first, as the appliance with the highest contribution brings the greatest impact to the total consumption. Then, DTW is applied to identify the series from selected highest power level cluster (label the cluster) and SSDTW to estimate the consumption trajectory of single appliance. This process is repeated until each appliance power consumption trajectory is reproduced except normally open appliances.