Status and challenges of residential and industrial non-intrusive load monitoring

Adabi, Ali; Mantey, Patrick E.; Holmegaard, Emil; Kjærgaard, Mikkel Baun

doi:10.1109/sustech.2015.7314344

Cited by 19 publications

(13 citation statements)

References 18 publications

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“…As such, the industrial DSM requires the integration of production and energy management programmes and a detailed consumption data analysis for developing DSM programmes tailored for each category. The key concern is that, generally, utility's DSM-related measurement equipment involves massive data collection and handling, which reduces industrial business owners' interests in DSM programmes [9]. They are usually concerned that DSM programmes may affect their core business and that the revenue stream is highly dependent on external factors like the results of electricity market actions.…”

Section: Introduction 1industrial Dsm and The Need For Load Disaggreg...mentioning

confidence: 99%

A Practical Load Disaggregation Approach for Monitoring Industrial Users Demand with Limited Data Availability

Tavakoli

Khalilpour

2021

Energies

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The emergence of smart sensors has had a significant impact on the utility industry. In particular, it has made the planning and implementation of demand-side management (DSM) programmes easier. Nevertheless, for various reasons, some users may not implement smart meters for load monitoring. This paper addresses such cases, particularly large-scale industrial users, which, despite heavy electrical loads coming from many different processes, implement only simple energy measuring equipment for billing purposes. This necessitates the utilisation of novel methodologies for load disaggregation, often referred to as nonintrusive load monitoring (NILM). The availability of such tools can create multifold benefits for industrial park management, utility service providers, regulators, and policymakers. Here, we introduce an optimisation algorithm for nonintrusive load disaggregation that is low-cost, speedy, and acceptably accurate. As a case study, we used real network data of three industrial sectors: food processing, stonecutting, and glassmaking. For all cases, the optimisation framework developed a desegregated profile and estimated the load with an error of less than 5%. For non-workdays, given the higher uncertainty for the continuity of different processes, the estimation error was higher but still in an acceptable range of around 3.63–15.09% with an average of 8.10%.

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Section: Introduction 1industrial Dsm and The Need For Load Disaggreg...mentioning

confidence: 99%

A Practical Load Disaggregation Approach for Monitoring Industrial Users Demand with Limited Data Availability

Tavakoli

Khalilpour

2021

Energies

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“…The development of non-intrusive load monitoring (NILM) can improve the ability of managers to manage energy. However, the industrial and commercial application of NILM technology has not been well developed compared to its widespread application in household load monitoring [1].…”

Section: Introductionmentioning

confidence: 99%

A non‐intrusive load state identification method considering non‐local spatiotemporal feature

Zhang

Duan

et al. 2021

IET Generation Trans & Dist

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This paper presents a non-intrusive method for identifying the load state of a distribution network. The method focuses on continuously varying loads. By considering the load onoff state switching points and the continuous features at on state, a deep convolutional method considering non-local spatiotemporal features is proposed. The addition of an attention component to the convolutional network enhances the non-local feature extraction capability of the convolutional network. Ultimately, the effectiveness of the method is demonstrated in an experimental setting. In addition, this paper demonstrates that the proposed method can effectively integrate switching point features as well as persistent features through neural network visualization techniques.

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“…Non-intrusive load monitoring (NILM) using smart meter readings is an ambient intelligence solution for dementia care, which brings benefits from alleviating the assistance burden of caregivers to monitoring the activities of daily life (ADLs) of patients and timely detecting abnormal behaviours of patients, and to alerting the progress of the disease. Smart meter big data are considered as a cost-effective source to achieve NILM with the rolling out of smart meters [5]. Using electricity consumption data (e.g.…”

Section: Introductionmentioning

confidence: 99%

A telehealth framework for dementia care: an ADLs patterns recognition model for patients based on NILM

Dai

Wang

Meng

2021

2021 International Joint Conference on Neural Networks (IJCNN)

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The ageing of the population and the increasing number of patients with dementia in modern society undoubtedly put tremendous pressure on the medical system. Providing telehealth care for potential patients and patients with dementia can reduce the burden on both the health system and caregivers. This paper describes a telehealth framework for dementia early detection and dementia care. Specifically, we propose an improved deep neural network model for Non-Intrusive Load Monitoring (NILM), which disaggregates the household's overall energy usage into those of individual appliances based on the sequence-to-point model and transfer learning. The daily behaviour regularities of patients are then inferred by combining principal component analysis and K-means clustering based on the disaggregated appliance-level consumptions. Experiments show that the proposed model can significantly improve training efficiency and maintain load disaggregation accuracy, and the inferred behaviour regularities have great potential to be used as useful inputs and prior knowledge to the dementia condition detection platform for early detection and real-time monitoring of patient's conditions.

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Status and challenges of residential and industrial non-intrusive load monitoring

Cited by 19 publications

References 18 publications

A Practical Load Disaggregation Approach for Monitoring Industrial Users Demand with Limited Data Availability

A Practical Load Disaggregation Approach for Monitoring Industrial Users Demand with Limited Data Availability

A non‐intrusive load state identification method considering non‐local spatiotemporal feature

A telehealth framework for dementia care: an ADLs patterns recognition model for patients based on NILM

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