This paper presents a novel Non-Intrusive Load Monitoring (NILM) approach focusing on the Energy Efficiency (EE) assessment of residential appliances. This approach (NILMEE) is able to identify the individual consumption of several household devices, providing proper information for evaluating energy efficiency and pointing out the operational issues or labelling mismatches of appliances, while recommending better practices for energy usage in specific consumer installations. The proposed approach was developed and evaluated by embedding the NILM engine on an electronic power meter, which performs a microscopic analysis on measured voltages and currents and provides the load disaggregation using the Conservative Power Theory for the feature extraction, K-Nearest Neighbours for the appliance classification, and the Power Signature Blob for the energy disaggregation. The disaggregation algorithm performance evaluation is carried out using NILMTK. Results show that NILM transcends the regular energy usage calculation, serving as a tool that enables the diagnosis of household appliances using the energy efficiency indexes provided by labels and standards.
Worldwide, energy efficiency policies can bring significant advantages to energy consumers and suppliers, leading to social, environmental, and economic benefits. Energy Efficiency (EE) may be an important resource to increase energy supply, as the first response to bigger demands and economic development (WEC 2013; IEA 2016). Therefore, the growing concern about improving EE has led to the creation of many new technologies, such as those related to energy management to control peak energy demand, better use of home appliances and the development of increasingly more efficient home appliances. Trotta (2018). These may be effective actions to optimise modern smart grid operations, supporting both consumers and electric utilities toward the energy efficiency perspective. Moreover, considering that residential consumers may represent over 30% of the energy consumption in some countries, household appliances may interfere with the overall energy efficiency. Special attention has been paid to heating and cooling devices, leading to energy-consuming loads in residential installations (Pino-Mejías et al. 2017).
Thus, improvements in EE in residential installations may be essential for increasing the overall efficiency of the power grid and, traditionally, it basically relies on providing detailed information on the energy usage and efficiency of individual household appliances (Jin et al. 2017). The most common approaches are based on energy labelling initiatives, which serve not only as guides for consumers but also to stimulate innovative product development for complying with lower rates of energy consumption (Issock et al. 2018). Energy efficiency labels help consumers assess the appliance’s classification under particular operating conditions, such as those defined in standards and national programs (Wong and Krüger 2017; Merini et al. 2019). However, recent studies show that the labelling approach may not be enough for achieving expected goals in different countries (EC 2019) and further initiatives are required.
Strategies for increasing energy efficiency may be based on Non-Intrusive Load Monitoring (NILM) (Hart 1992), which was introduced decades ago, however the reduced computational power may have limited its practical applications (Makonin 2012). Considering modern computing engines and tools, NILM has attracted widespread attention, and the recent efforts focus on the proper use of artificial intelligence to create solutions for home and industrial energy management systems, demand-side management, and identifying appliances (Hart 1992; Aboulian et al. 2019; Garcia et al. 2017). NILM represents a powerful tool to disaggregate the energy consumption of electrical installations, which can be embedded in modern energy meters (Souza et al. 2019; Zhang et al. 2018), providing information for assessing energy consumption in terms of household appliances.
Considering the load consumption disaggregation and based on the increasing energy-awareness of individual equipment, consumers may adapt consumption behaviours, replace equipment or install management systems focusing on energy/money savings (Baets et al. 2017; Mack et al. 2019), either on residential, commercial or industrial scenarios (Sadeghianpourhamami et al. 2017; Henriet et al. 2018; Stankovic et al. 2016). Recent NILM studies have been based on different attribute extraction methods, accuracy evaluations, and load disaggregation results ranging from 70% to 98% (Souza et al. 2019; Sadeghianpourhamami et al. 2017; Esa et al. 2016; Wong et al. 2013; Abubakar et al. 2015; Le and Kim 2018; Aladesanmi and Folly 2015; Klemenjak 2018; Bao et al. 2018; Gopinath et al. 2020).
However, most studies in the NILM literature are focused only on the analysis of the energy disaggregation concept and do not take into account the correlation with energy efficiency (Sadeghianpourhamami et al. 2017; Ruano et al. 2019). Few studies in the literature indicate the use of the NILM approach for EE assessment in the household scenario (Kong et al. 2020) and some of them focus on indicating high-consumption loads (Doherty and Trenbath 2019; Kong et al. 2020; Souza et al. 2020), feedback and recommendations to the consumer (Carrie Armel et al. 2013; Berbakov et al. 2019; Kong et al. 2020), and anomaly detection regarding the default appliance’s power signature (Rashid et al. 2019). However, there are no studies that compare the appliance EE label indexes with short and long-time use of operation. Hence, with this gap in mind, this paper introduces a novel NILM-based approach called NILMEE (NILM for EE), which can disaggregate the energy demand of appliances of interest, while analysing the EE of each appliance in terms of a national labelling program.
The developed strategy was mainly based on the K-Nearest Neighbours (KNN) algorithm (Cover and Hart 1967) to identify the load and the Conservative Power Theory (CPT) (Tenti et al. 2011) as the background for the feature extraction. Moreover, this paper proposes a novel and oversimplified approach for energy disaggregation based on the Power Signature Blob (PSB) introduced by Souza et al. 2019.
This paper differs from previous proposals by introducing the use of NILM for energy efficiency assessment to detect appliances’ consumption deviations while providing maintenance or proper usage recommendations for household consumers. The proposed NILMEE engine was embedded on a smart meter prototype, indicating how such devices would perform the microscopic analysis of household appliances, thus being much more useful than merely for monthly energy measuring or on-off control.
The following sections briefly review the NILM approach, describe the evolution of the PSB concept, show the EE assessment and performance analysis of the NILM purpose, present the NILMEE validation, and depict the results of NILMEE applications in a case study of a real household.