Nonintrusive load monitoring (NILM) is an important technique for energy management and conservation. In this paper, a deep learning model based on an attention mechanism, temporal pooling, residual connections, and transformers is proposed. This art...
Non-intrusive Load Monitoring (NILM) is a critical technology that enables detailed analysis of household energy consumption without requiring individual metering of every appliance, and has the capability to provide valuable insights into energy usa...
Non-intrusive load monitoring is a blind source separation task that has been attracting significant interest from researchers working in the field of energy informatics. However, despite the considerable progress, there are a very limited number of...
Numerous approaches exist for disaggregating power consumption data, referred to as non-intrusive load monitoring (NILM). Whereas NILM is primarily used for energy monitoring, we intend to disaggregate a household’s power consumption to detect...
The central problems of some of the existing Non-Intrusive Load Monitoring (NILM) algorithms are indicated as: (1) higher required electrical device identification accuracy; (2) the fact that they enable training over a larger device count; and (3) t...
Non-intrusive load monitoring (NILM) has become an important subject of study, since it provides benefits to both consumers and utility companies. The analysis of smart meter signals is useful for identifying consumption patterns and user behaviors,...
A NILM dataset is a valuable tool in the development of Non-Intrusive Load Monitoring techniques, as it provides a means of evaluation of novel techniques and algorithms, as well as for benchmarking. The figure of merit of a NILM dataset includes cha...
For many years, energy monitoring at the most disaggregate level has been mainly sought through the idea of Non-Intrusive Load Monitoring (NILM). Developing a practical application of this concept in the residential sector can be impeded by the techn...
A multi-agent architecture for a Non-Intrusive Load Monitoring (NILM) solution is presented and evaluated. The underlying rationale for such an architecture is that each agent (load event detection, feature extraction, and classification) outperforms...
Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used to devise load scheduling strategies for optimal energy utilization. Fine-...
In traditional nonintrusive load monitoring (NILM) systems, the measurement device is installed upstream of an electrical system to acquire the total aggregate absorbed power and derive the powers absorbed by the individual electrical loads. Knowing...
Non-intrusive load monitoring (NILM) is an approach that helps residents obtain detailed information about household electricity consumption and has gradually become a research focus in recent years. Most of the existing algorithms on NILM build ener...
With global efforts intensifying towards achieving carbon neutrality, accurately monitoring and managing energy consumption in industrial sectors has become critical. Non-Intrusive Load Monitoring (NILM) technology presents a cost-effective solution...
The measurement of the energy consumption of electrical appliances, where the meter is installed at a single point on the main input circuit of the building, is called non-intrusive load monitoring (NILM). The NILM method can distinguish the loads th...
A non-intrusive load monitoring (NILM) process is intended to allow for the separation of individual appliances from an aggregated energy reading in order to estimate the operation of individual loads. In the past, electricity meters specified only a...
Advanced Non-Intrusive Load Monitoring (NILM) research is important to help reduce energy consumption. Very-low-frequency approaches have traditionally faced challenges in separating appliance uses due to low discriminative information. The richer si...
Energy disaggregation, or nonintrusive load monitoring (NILM), is a technology for separating a household’s aggregate electricity consumption information. Although this technology was developed in 1992, its practical usage and mass deployment h...
Non-Intrusive Load Monitoring (NILM) technology, enabled by high-precision electrical data acquisition sensors at household entry points, facilitates real-time monitoring of electricity consumption, enhancing user interaction with smart home systems...
Energy disaggregation algorithms disintegrate aggregate demand into appliance-level demands. Among various energy disaggregation approaches, non-intrusive load monitoring (NILM) algorithms requiring a single sensor have gained much attention in recen...
To achieve the goal of carbon neutrality, the demand for energy saving by the residential sector has witnessed a soaring increase. As a promising paradigm to monitor and manage residential loads, the existing studies on non-intrusive load monitoring...
Next-generation power systems aim at optimizing the energy consumption of household appliances by utilising computationally intelligent techniques, referred to as load monitoring. Non-intrusive load monitoring (NILM) is considered to be one of the mo...
Non-intrusive load monitoring (NILM) is an important research direction and development goal on the distribution side of smart grid, which can significantly improve the timeliness of demand side response and users’ awareness of load. Due to rap...
Non-Intrusive Load Monitoring (NILM), a technique that extracts appliance-level energy consumption information through analysis of aggregated electrical measurements, has become essential for smart grids and energy management applications. Given the...
The useful planning and operation of the energy system requires a sustainability assessment of the system, in which the load model adopted is the most important factor in sustainability assessment. Having information about energy consumption patterns...
Datasets are important for researchers to build models and test how these perform, as well as to reproduce research experiments from others. This data paper presents the NILM Performance Evaluation dataset (NILMPEds), which is aimed primarily at rese...
Non-Intrusive Load Monitoring (NILM) describes the process of inferring the consumption pattern of appliances by only having access to the aggregated household signal. Sequence-to-sequence deep learning models have been firmly established as state-of...
Non-intrusive load monitoring (NILM), as a key technology in smart-grid advanced metering infrastructure, aims to disaggregate mains power from smart meters into individual load-level power consumption. Traditional NILM methods require centralizing s...
A crucial step in nonintrusive load monitoring (NILM) is feature extraction, which consists of signal processing techniques to extract features from voltage and current signals. This paper presents a new time-frequency feature based on Stockwell tran...
Energy-saving schemes are nowadays a major worldwide concern. As the building sector is a major energy consumer, and hence greenhouse gas emitter, research in home energy management systems (HEMS) has increased substantially during the last years. On...
This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e., data with sampling rates lower than the AC base frequency. The overall purpose of this revie...
Datasets are important for researchers to build models and test how well their machine learning algorithms perform. This paper presents the Rainforest Automation Energy (RAE) dataset to help smart grid researchers test their algorithms that make use...
Non-Intrusive Load Monitoring (NILM) techniques are effective for managing energy and for addressing imbalances between the energy demand and supply. Various studies based on deep learning have reported the classification of appliances from aggregate...
Non-Intrusive Load Monitoring (NILM) includes a set of methods orientated to disaggregating the power consumption of a household per appliance. It is commonly based on a single metering point, typically a smart meter at the entry of the electrical gr...
Non-Intrusive Load Monitoring (NILM) refers to the analysis of the aggregated current and voltage measurements of Home Electrical Appliances (HEAs) recorded by the house electrical panel. Such methods aim to identify each HEA for a better control of...
Nonintrusive load monitoring (NILM) is a process that disaggregates individual energy consumption based on the total energy consumption. In this study, an energy disaggregation model was developed and verified using an algorithm based on a recurrent...
Non-intrusive load monitoring systems that are based on deep learning methods produce high-accuracy end use detection; however, they are mainly designed with the one vs. one strategy. This strategy dictates that one model is trained to disaggregate o...
In this paper, a novel method that utilizes the fractional correlation-based algorithm and the B-spline curve fitting-based algorithm is proposed to extract the complementary features for detecting the operating states of appliances. The identificati...
Series arc faults cause the majority of household fires involving electrical failures or malfunctions. Low-fault current amplitude is the reason for the difficulties faced in implementing effective arc detection systems. The paper presents a novel ar...
Since decades past, time–frequency (TF) analysis has demonstrated its capability to efficiently handle non-stationary multi-component signals which are ubiquitous in a large number of applications. TF analysis us allows to estimate physics-rela...
Non-Intrusive Load Monitoring (NILM) enables appliance-level energy analysis from aggregated electrical signals, offering valuable insights for smart energy systems. While most NILM research focuses on high-resource environments, this study evaluates...
Existing non-intrusive load monitoring (NILM) methods predominantly rely on centralized models, which introduce privacy vulnerabilities and lack scalability in large industrial park scenarios equipped with distributed energy resources. To address thi...
Climate change, primarily driven by human activities such as burning fossil fuels, is causing significant long-term changes in temperature and weather patterns. To mitigate these impacts, there is an increased focus on renewable energy sources. Howev...
Demand side management has a vital role in supporting the demand response in smart grid infrastructure, in the decision-making of energy management, in household applications is significantly affected by the load-forecasting accuracy. This paper intr...
The paper presents a novel method for non-intrusive appliances identification. It can be used for energy load disaggregation in a smart grid. The approach identifies changes in the state of the particular appliance by measuring and processing the com...
The massive deployment of smart meters in most Western countries in recent decades has allowed the creation and development of a significant variety of applications, mainly related to efficient energy management. The information provided about energy...
This study investigates residential electricity consumption behaviors in the Moldova region of Romania, with a focus on identifying consumption patterns through a non-invasive, survey-based approach. Unlike intrusive monitoring or smart metering meth...
The research area of NILM exhibits a high heterogeneity regarding approaches and characteristics, especially in terms of the applied algorithms, measurement data, quantities, and features used, as well as congruent appliance event and state definitio...
In this article an energy disaggregation architecture using elastic matching algorithms is presented. The architecture uses a database of reference energy consumption signatures and compares them with incoming energy consumption frames using template...
Deploying energy disaggregation models in the real-world is a challenging task. These models are usually deep neural networks and can be costly when running on a server or prohibitive when the target device has limited resources. Deep learning models...
Electrification of transportation is gaining traction as a viable alternative to vehicles that use fossil-fuelled internal combustion engines, which are responsible for a major part of carbon dioxide emissions. This global turn towards electrificatio...
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