New Appliance Detection for Nonintrusive Load Monitoring

Zhang, Jianjun; Chen, Xuanqun; Ng, Wing W. Y.; Lai, Chun Sing; Lai, Loi Lei

doi:10.1109/tii.2019.2916213

Cited by 60 publications

(18 citation statements)

References 26 publications

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“…Secondly, the usage frequency of the target appliance should be analyzed. For example, if a household's aggregated data covers 1 month, and the target appliance was used only once during that period, sufficient information cannot be extracted [29]. To mitigate this problem, synthetic data generation, which is a method to augment the data by using the existing dataset, is used.…”

Section: Real-time Evaluation Of Different DL Modelsmentioning

confidence: 99%

Smart-Building Applications: Deep Learning-Based, Real-Time Load Monitoring

Çimen

García

Kolaek

et al. 2021

EEE Ind. Electron. Mag.

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Norvig said that "We don't have better algorithms than anyone else, we just have more data". This inspiring statement shows that having more data is directly related to better decision making and having the foresight about the future. With the development of the Internet of Things (IoT) technology, it is now much easier to gather data.Technological tools such as social media websites, smartphones, and security cameras can be considered as "data generators". When the focus is shifted to the energy field, these generators are "Smart Meters".Smart meter technology incorporates many intelligent functions and offers great opportunities for utility operators, prosumers, and consumers. Although smart meters are referred to as 'smart', they might not be intelligent enough depending on the final purpose.Meter data generally provide more benefits for the utility side than for the consumer side.However, with the smart meter data, customers can be offered great opportunities, where they may be able to make more conscious decisions. Previous studies have reported that if instantaneous energy consumption data are given to the consumers as feedback, approximately 20% of energy savings can be achieved per household [1]. To achieve this target, more detailed data on the electricity consumed by each appliance are needed. Smart meters cannot meet this

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Section: Real-time Evaluation Of Different DL Modelsmentioning

confidence: 99%

Smart-Building Applications: Deep Learning-Based, Real-Time Load Monitoring

Çimen

García

Kolaek

et al. 2021

EEE Ind. Electron. Mag.

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“…Disaggregating electrical device usage is called Appliance Load Monitoring (ALM) [85]. ALM is divided into two types: Non-Intrusive Load Monitoring (NILM) [86] and Intrusive Load Monitoring (ILM) [87]. NILM is a single point sensor, such as a smart meter or CT clip.…”

Section: Load Disaggregationmentioning

confidence: 99%

Energy Sobriety: A Behaviour Measurement Indicator for Fuel Poverty Using Aggregated Load Readings from Smart Meters

Fergus

Chalmers

2021

Towards Energy Smart Homes

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Fuel poverty affects between 50 and 125 million households in Europe and is a significant issue for both developed and developing countries globally. This means that fuel poor residents are unable to adequately warm their home and run the necessary energy services needed for lighting, cooking, hot water, and electrical appliances. The problem is complex but is typically caused by three factors; low income, high energy costs, and energy inefficient homes. In the United Kingdom (UK), 4 million families are currently living in fuel poverty. Those in series financial difficulty are either forced to self-disconnect or have their services terminated by energy providers. Fuel poverty contributed to 10,000 reported deaths in England in the winter of 2016-2107 due to homes being cold. While it is recognized by governments as a social, public health and environmental policy issue, the European Union (EU) has failed to provide a common definition of fuel poverty or a conventional set of indicators to measure it. This chapter discusses current fuel poverty strategies across the EU and proposes a new and foundational behavior measurement indicator designed to directly assess and monitor fuel poverty risks in households using smart meters, Consumer Access Device (CAD) data and machine learning. By detecting Activities of Daily Living (ADLS) through household appliance usage, it is possible to spot the early signs of financial difficulty and identify when support packages are required.

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“…There is always an imbalance in the data set in NILM, but appropriate techniques can be used to improve the robustness and effectiveness of the system. Zhang [1] adopts a stochastic sensitivity measure-based noise filtering and oversampling (SSMNFOS) training base classifier to form a multi-class set of neural network and perform multi-label classification for NILM. Compared with the famous oversampling method, the synthetic minority oversampling technique (SMOTE) has reached remarkable performance in the actual noninvasive load monitoring application.…”

Section: Unbalanced Classificationmentioning

confidence: 99%

A pseudo Karnaugh mapping approach for datasets imbalance

Liu

Liao

2021

E3S Web Conf.

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The problem of dataset imbalance has raised a wide concern in many machine learning areas, but not in non-intrusive load monitoring, or load disaggregation. In this study, a pictorial evaluation method is proposed to representation the imbalance class distribution in datasets. We colored a Karnaugh maps according to the quantities of different variables combination to offer a visual impact to the whole dataset. After utilizing this method on a public dataset and its testing result, a clear imbalanced abundance in the dataset and an exciting performance have been found. A preliminary Python package to realize this mapping method has been uploaded on GitHuba.

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New Appliance Detection for Nonintrusive Load Monitoring

Cited by 60 publications

References 26 publications

Smart-Building Applications: Deep Learning-Based, Real-Time Load Monitoring

Smart-Building Applications: Deep Learning-Based, Real-Time Load Monitoring

Energy Sobriety: A Behaviour Measurement Indicator for Fuel Poverty Using Aggregated Load Readings from Smart Meters

A pseudo Karnaugh mapping approach for datasets imbalance

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