RETRACTED: A review disaggregation method in Non-intrusive Appliance Load Monitoring

Esa, Nur Farahin; Abdullah, Pauzi; Hassan, Mohammad Yusri

doi:10.1016/j.rser.2016.07.009

Cited by 103 publications

(60 citation statements)

References 48 publications

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“…Through continuously monitoring the state of environment, such as daylight [47], external temperature [48], added glazed façade [49], trigeneration energy system [50], system energy savings could be achieved. These systems need the assistance of simulation or computing software for precise calculation and estimation, such as daylight control system, IDA Indoor Climate and Energy software (IDA-ICE), building management systems (BMSs), etc.…”

Section: Continuous Monitoring Technology With Multi-parameters Sementioning

confidence: 99%

Enabling Smart Air Conditioning by Sensor Development: A Review

Cheng

Lee

2016

Sensors

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The study investigates the development of sensors, in particular the use of thermo-fluidic sensors and occupancy detectors, to achieve smart operation of air conditioning systems. Smart operation refers to the operation of air conditioners by the reinforcement of interaction to achieve both thermal comfort and energy efficiency. Sensors related to thermal comfort include those of temperature, humidity, and pressure and wind velocity anemometers. Improvements in their performance in the past years have been studied by a literature survey. Traditional occupancy detection using passive infra-red (PIR) sensors and novel methodologies using smartphones and wearable sensors are both discussed. Referring to the case studies summarized in this study, air conditioning energy savings are evaluated quantitatively. Results show that energy savings of air conditioners before 2000 was 11%, and 30% after 2000 by the integration of thermo-fluidic sensors and occupancy detectors. By utilizing wearable sensing to detect the human motions, metabolic rates and related information, the energy savings can reach up to 46.3% and keep the minimum change of predicted mean vote (∆PMV→0), which means there is no compromise in thermal comfort. This enables smart air conditioning to compensate for the large variations from person to person in terms of physiological and psychological satisfaction, and find an optimal temperature for everyone in a given space. However, this tendency should be evidenced by more experimental results in the future.

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Section: Continuous Monitoring Technology With Multi-parameters Sementioning

confidence: 99%

Enabling Smart Air Conditioning by Sensor Development: A Review

Cheng

Lee

2016

Sensors

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“…The combination of the need for technologies promoting low-carbon emissions and the advances in machine learning and statistical techniques is generating a substantial amount of energy disaggregation review papers such as Nalmpantis and Vrakas (2018), Esa, Abdullah, and Hassan (2016), Abubakar, Khalid, Mustafa, Shareef, and Mustapha (2016), Wong, Ahmet Sekercioglu, Drummond, and Wong (2013), Butner, Reid, Hoffman, Sullivan, and Blanchard (2013), Makonin (2012), Zoha, Gluhak, Imran, and Rajasegarar (2012), Jiang, Li, Luo, Jin, and West (2011), and Zeifman and Roth (2011). In general NILM approaches are grouped into two categories: (a) event-based approaches and (b) event-less approaches (Bergés & Kolter, 2012).…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A review

Pereira¹,

Nunes

2018

WIREs Data Min & Knowl

133

103

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Non-intrusive load monitoring (also known as NILM or energy disaggregation) is the process of estimating the energy consumption of individual appliances from electric power measurements taken at a limited number of locations in the electric distribution of a building. This approach reduces sensing infrastructure costs by relying on machine learning techniques to monitor electric loads. However, the ability to evaluate and benchmark the proposed approaches across different datasets is key for enabling the generalization of research findings and consequently contributes to the large-scale adoption of this technology. Still, only recently researchers have focused on creating and standardizing the existing datasets in order to deliver a single interface to run NILM evaluations. Furthermore, there is still no consensus regarding, which performance metrics should be used to measure and report the performance of NILM systems and their underlying algorithms. This paper provides a review of the main datasets, metrics, and tools for evaluating the performance of NILM systems and technologies. Specifically, we review three main topics: (a) publicly available datasets, (b) performance metrics, and (c) frameworks and toolkits. The review suggests future research directions in NILM systems and technologies, including cross-datasets, performance metrics for evaluation and generalizable frameworks for benchmarking NILM technology.

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“…This problem is the cheap China-made electrical appliances (such as cheap LED bulbs), which are provided in the most part of the available electrical appliance markets. Electrical lighting has a big part of electrical power consumption in the household sector, as it can be seen in [3]. Most of the electrical appliances (fans, refrigerators, TV, lighting etc.)…”

Section: Introductionmentioning

confidence: 99%

“…do not have only active electrical power character and it is PF, not 1. As the consumer survey shows, consumers try to save money by changing electrical bulbs to powersaving bulbs available in the market [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reactive electrical power compensation in household sector

Jakusenoks¹,

Laizans²

2017

Engineering for Rural Development

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Abstract. The article presented is devoted to the research of the household sector reactive electrical power compensation. Power factor (PF) is changing the value due to the active and reactive electrical power change. Some of electrical appliances have a very low power factor (about 0.4). Electrical power grid encounters with a noise and fluctuation problems that are caused by reactive electrical power. The research provided shows some reactive electrical power compensation methods and MatLab Simulink example model, which has been made for the research object. Some of the reactive electrical power compensation models need long-term electrical consumption information collection, where the power supplier's company has access to, or the electrical power consumers, who are interested in the electrical power optimization and reactive electrical power compensation. Some of the reactive electrical power methods presented are usable in the all electrical consumer sectors, such as household, industrial, building, producers, selling etc. The article shows the benefits and disadvantages of these methods, what must be evaluated in the reactive electrical power compensation method. The described conclusions show the benefits, what electrical suppliers and total electrical users can gain. As an example, voltage loss decreases by 4 %, or total electrical grid current decreases by 3 % by full reactive electric power compensation, and the transformer load decreases by 5 % at 0.95. The values can change due to the load and the compensation method used.

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RETRACTED: A review disaggregation method in Non-intrusive Appliance Load Monitoring

Cited by 103 publications

References 48 publications

Enabling Smart Air Conditioning by Sensor Development: A Review

Enabling Smart Air Conditioning by Sensor Development: A Review

Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A review

Reactive electrical power compensation in household sector

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