*1.4. Actionable Recommendations*

After disaggregation, the appliance usage pattern of consumers should be analyzed to ascertain opportunities for energy savings or PQ improvement and should recommend the same. The consumer may decide to act upon these recommendations. The consumer benefit lies in the reduction in the unnecessary usage of high-power appliances such as geysers, air conditioning and HVAC (heating, ventilation, and air conditioning). However, the power ratings of these appliances are becoming greener and smarter day by day. At the same time, energy savings on account of the usage of low-power appliances (SMPS, mobile chargers, CFL (compact fluorescent lamp), LED) are not remunerative enough to the consumer. By reviewing the previous literature [29], Kelly, J. et al. assessed that the reduction in domestic electricity consumption on average is 0.7–4.5% only. Usually, consumers are conscious about the usage of high-power appliances, whereas low-power appliances are not considered from an energy savings point of view. Zhuang, M. [30] highlights that there are other good reasons why we need NILM of appliances that employ electronic controls for load demand forecasting accuracy, to provide better criteria for utilities to decide on generation. For the grid operators, NILM additionally allows flexible resource management for DR and tackles the uncertainty derived from renewable sources, apart from energy savings.

#### *1.5. Summary and Proposal*

Interestingly, all of the research is focused on energy savings from the high-power appliances of a household, which are usually not a big concern for utilities. The simplification of the DR approach with reasonable accuracy and reliability is the main characteristic that drives its adoption. A schematic of the DR management system is shown in Figure 1, and the generic process is self-explanatory.

**Figure 1.** Schematic of Demand Response (DR) management.

The literature suggests that harmonics, harmonic phasors and percentage THD features are used for NILM. A.S. Bouhouras et al. [31] employed a simple lookup table and used the summation of the first three odd harmonic phasors. A non-intrusive discriminant analysis of loads based on PQ data was reported in [32], where classifiers based on linear and quadratic discriminant analysis were implemented. Household appliance classification using lower odd-numbered harmonics and the bagging decision tree approach was detailed in [33]. A.S. Bouhouras et.al [34] employed entropies and spectral band energy to specified frequency bands of the current spectrum to simplify the identification of appliances and accomplished good results. All of these methods adopted deep learning, machine learning and/or statistical methods. Moreover, these approaches considered DR management from an energy conservation perspective but not from a PQ perspective.

Consumers are not paying for the harmonic power induced by nonlinear appliances, which are up to 20% above their real consumption, as energy meters ignore harmonic currents. Harmonics, due to the low electronic power loads of consumers, are disregarded due to their low power consumption; however, the harmonic diction levels are very high. This issue was highlighted by a survey report in 2015 by the Government of India in the name of Swachh Power [35]. There are various ISO(International Organization for Standardization) standards and guidelines such as IEC (International Electrotechnial Commission) 61000-3-2, 61000-4-30 and IEEE (The Institute of Electrical and Electronics Engineers) 519-2014 for harmonic estimation and control [35].

The THD percentages of loads were established by O Deepu et.al [36] for three different loads—CFL, personal computer, and uninterrupted power supply—and were found to be distinct. The percentage THD of current harmonics for these loads is found to be 72, 125, and 35, respectively. Marko Dimitrijevi´c [17] measured the first 39 harmonics for six different appliances and various combinations of these appliances in operation.

In the opinion of authors of this article, the quality of power is more critical for utilities than for power savings in smart homes. The authors would like to approach DR from a PQ point of view rather than to reduce power consumption. If any conventional methods or intrusive methods are used to identify the load patterns, they would not throw light on the harmful impact of harmonics introduced by the pervasive use of low-power nonlinear loads, and hence would not lead to improving PQ from a harmonics perspective. Two standards, namely, IEEE 519-2014 and IEC 61000-3-2 and the Central Electricity Authority guidelines recommend reducing the percentage THD from both utility and consumer perspectives. The understanding of the authors of this article is that this approach is very much needed to impress upon consumers and utilities the urgency of this issue. The industry can adopt this solution if percentage THD is measured, and we offer insights into further actions that can be taken.

Low-power non-linear load harmonic compensation is not addressed individually; therefore, the authors understand that accurate estimation of low-power harmonics and their corresponding percentage THD will identify the consumer loads effectively. The authors propose to use the percentage THD of steady-state harmonics, which is distinct for individual appliances and different combinations of appliances. In this article, the authors propose a novel solution that uses the uniqueness of percentage THD to identify which set of appliances is in operation at any point in time without any ambiguity. This solution is the first of its kind, to the best of our knowledge, that establishes the efficient disaggregation of energy consumption at the appliance level. DR management using percentage THD is depicted in Figure 2.

**Figure 2.** DR management in view of load monitoring using % Total Harmonic Distortion (THD).

NILM has been applied mostly to high-power appliances (50 W and above) from a power conservation perspective. There is insufficient research on percentage THD conservation induced by low-power nonlinear loads (less than 100 W), which is a big challenge of today in our distribution systems. For the first time, we tackle a problem that is not addressed as required by IEEE 519-2014, IEC 61000-3-2 standards and Central Electricity Authority guidelines. NILM is discussed in this article to bring the state-of-the-art research into context and to contrast with the proposed approach.
