NILM-based approach for energy efficiency assessment of household appliances

Abstract

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.

Recursive training based physics-inspired neural network for electric water heater modeling

“…Non-intrusive load monitoring (NILM) can be used as a correcting strategy for the case where EWH power consumption data is not available after simulation. NILM is a technique to disaggregate individual appliance power consumption from total power consumption from household smart meter measurements (Garcia et al 2020;Zufferey et al 2020). As aggregators can have access to total consumption through smart meter data, it is possible to infer EWH consumption to a good extent from total power consumption using NILM techniques.…”

Efficient Supervised Machine Learning Network for Non-Intrusive Load Monitoring

“…Energy management systems to regulate peak energy demand [3] are examples of new technologies that have been developed to enhance EE. With a population of 67.22 million (2020), increasing the overall efficiency of the electricity grid by boosting EE in residential areas may be crucial [3]. Furthermore, giving precise information on the energy use of consumer appliances will enhance the EE.…”

“…Furthermore, giving precise information on the energy use of consumer appliances will enhance the EE. When considering the disaggregation of load consumption and the increased energy awareness of particular equipment, users can change their consumption behaviour, replace equipment, or install energy management systems to save energy or money [3,4].…”

“…Technologies 2022, 10, 85 2 of 18 equipment, users can change their consumption behaviour, replace equipment, or install energy management systems to save energy or money [3,4]. The development of new buildings in cities throughout the world has transformed dwelling arrangements and boosted the demand for end-use appliances with energy conservation and control [5].…”

Impact of nonintrusive load monitoring on CO₂ emissions in Malaysia

“…Apart from microscopic variables, the studies also conducted the relationship between the determinant variable with macroscopic variables such as gross domestic product GDP, population, urbanisation [10], and the single variable using GDP [13]. Several studies of scenario simulation method analyse the impacts of possible future CO2 emissions by considering several alternative settings of predictors [14]- [17] Previous literature provided good references to predicting CO2 emissions and the issues of electricity conservation due to NILM [18]- [21]. However, a literature gap still exists regarding the study of NILM impact on CO2 emissions, to the best of our knowledge.…”