1. Introduction
Since the energy crisis and environmental problems caused by greenhouse emissions have become critical issues [
1,
2], smart grids have been developing globally. Electrical power utilization is a significant part of the smart grid [
3]. Studies have shown that detailed feedback on electricity consumption can achieve 20–35% energy savings [
4]. From the perspective of energy consumers, load monitoring can promote the energy efficiency of end users. In the context of smart grids, the energy consumption details of appliance usage could help to specify demand response strategies, which would make it possible to reduce peak demands by reducing consumption or by taking advantage of non-peak times [
5]. The details of energy consumption help to achieve short-term and long-term forecasts of electricity habits, and help to manage energy distribution regarding the integration of more fluctuating energy, such as renewable energy [
6].
Traditional technology, and intrusive load monitoring (ILM), usually require expensive measurement hardware and simple software. Each appliance is equipped with a sensor to monitor the power consumption, which is uploaded to the server over the network. On the other hand, non-intrusive load monitoring (NILM) technology, also called
load disaggregation, derives the electricity detail of each appliance from the total power consumption measured at the service entry by using pattern recognition technologies and machine learning algorithms. NILM offers low hardware cost and can be implemented quickly [
7].
NILM, proposed by Hart of MIT in 1992 [
8], has been extensively developed in recent years [
7]. Technically, NILM load disaggregation requires that the total energy consumption curve can reflect changes of individual appliances. In previous research, the NILM methods that utilize high-frequency signatures (frequency higher than 1 kHz) have achieved excellent performance in load identification. In the literature [
9], 1–16th harmonic characteristics are extracted to identify the combinations of different appliances using the neural network. The steady-state current is analyzed, and filters are designed to realize the load operation states by using the frequency domain characteristics of current [
10]. The authors of [
11] utilized the transient signature curve when appliances were switched on or off to identify the appliance. Current and voltage waveforms are used to realize load identification [
12,
13]. However, when the above methods are adopted, extra measurement hardware is necessary to obtain the high-frequency signatures of appliances, which increases hardware requirements [
14].
With the increasing use of detailed information of smart grids, smart meters are becoming an essential component of smart grids. Since low-frequency signatures are readily available from smart meters, the load disaggregation methods using low-frequency features are an essential development trend of NILM. Many studies use low-frequency features and have achieved excellent results [
15,
16,
17,
18,
19,
20]. In the literature [
15,
16,
20], hidden Markov model-based methods are used to decompose low-frequency total powers. However, since the computational complexity increases significantly with the increase in the number of appliances, these methods only identify two-state appliances. In [
17], the low-frequency total powers are clustered. As a consequence, the most significant contribution appliances are iteratively decomposed, but the previous results will affect the identification of low-power appliances. In [
18], naïve Bayesian estimation is used to classify and identify the combination of the active power of different appliances; the results show that the method has a limit in identifying the appliances with similar power, and that the power variation of the appliances during operation is not considered. In [
19], a current with little change during appliance operation is chosen as the load signature, and the differential evolution algorithm is used for decomposition. However, the decomposition model did not consider the change of the steady-state characteristics of appliances.
Aiming at the problems existing in the previous research of the NILM methods, this paper proposed a load disaggregation method based on a power consumption pattern for low sampling data. The proposed method exploited the power consumption patterns signature of appliances during operation, and presented an improved load decomposition model. To improve the global optimization ability of our method, the bird swarm algorithm (BSA) is optimized and applied to calculate the load decomposition results. Experiments proved the effectiveness of the proposed method.
The remaining sections of the paper are structured as follows: firstly,
Section 2 analyzes the load signature.
Section 3 describes the details of the proposed method. Then, the proposed method is tested and compared with existing methods in
Section 4. Finally,
Section 5 summarizes the conclusions.
2. Load Signature Analysis
The selection of load signature is the key to NILM technology and will directly affect the accuracy of load disaggregation. According to previous research, when multiple appliances with similar active and reactive power are operating, identification errors of NILM load disaggregation occur. Power fluctuations in appliances usually occur during operation due to the influence of electrical component characteristics, voltage instability and human activity, which causes inaccurate estimation of the power of appliances relative to their average power cosumption. The data from AMPds [
21] in
Figure 1 shows power changes during air conditioning operation. The air conditioner power values change from 1450 watts to 1820 watts in one operating cycle. Such power changes make some appliances (the power lower than 400 watts) challenging to identify.
In the literature, extra load signatures such as reactive power and high-frequency characteristics are used for load disaggregation to solve the above problems. However, there are still errors in identifying the appliances with similar PQ characteristics when only low-frequency power and active power are used. In [
22], the authors analyzed monitored power data and found that the power consumption pattern of appliances was useful in identifying them. The power sequences of typical household appliances from AMPds are extracted, and the power consumption curves in
Figure 2 are provided to illustrate their power consumption pattern. As can be seen from
Figure 2, the refrigerator and the furnace have similar power ranges, but the power curves have significant differences. Although distortions in the power consumption curve of each appliance are affected by power changes, human activities and other factors, each appliance has a regular and non-repeating power consumption pattern.
5. Conclusions
In this paper, with the problem of load disaggregation of using high-frequency features in non-intrusive load monitoring, a power consumption pattern-based method for low sampling data is proposed, which applied time coefficients to a traditional load disaggregation model and OBSA to optimization calculation. Experiments show that the method has a load decomposition accuracy of more than 94% for typical appliances, which can be used for the identification of similar power and normally-open appliances. The model achieved good performance in cases of multiple appliances simultaneously switching on and off.
As a load decomposition method using low-frequency features, it has better performance than the traditional mathematical optimization model using PQ features and saves computation time. Furthermore, a comparison of results with those reported in the literature proved the performance of the proposed method. Furthermore, the proposed method has better robustness at different sampling intervals, the convergence speed of the method is faster, and the convergence precision is better. The proposed method has the advantages of small hardware and software requirements for feature extraction, low sampling frequency requirements, high accuracy of load disaggregation, and suitability for use in smart grid scenarios.
Although the proposed method in this paper has the advantages mentioned above, it still has a limitation. It will misidentify the appliances whose power consumption pattern characteristic sequences are not obtained in the feature extraction stage. In order to identify unknown appliances, a feature library with as many appliances as possible can be built, but this will require a lot of work. Thus, further study will concentrate on improving the identification performance of unknown appliances.