Investigation of Deep Learning-based Techniques for Load Disaggregation, Low-Frequency Approach

Alkhulaifi, Abdolmaged; Aljohani, Abdulah Jeza

doi:10.14569/ijacsa.2020.0110186

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…For further comparison, the algorithm was run on UK-DALE dataset, that has been widely used in the literature [30,41,42,[51][52][53][54][55][56]. Data was collected from November 2012 to May 2015, with sampling periods of 1s for aggregated and 6s for disaggregated signals.…”

Section: Resultsmentioning

confidence: 99%

Non-Intrusive Load Monitoring (NILM) for Energy Disaggregation Using Soft Computing Techniques

et al. 2020

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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, in order to make predictions and optimizations to anticipate the use of electrical appliances at home. However, the problem with this kind of analysis rests in how to isolate individual appliances from an aggregated consumption signal. In this work, we propose an unsupervised disaggregation method based on a controlled dataset obtained using smart meters in a standard household. By using soft computing techniques, the proposed methodology can identify the behavior of each of the devices from aggregated consumption records. In the approach developed in this work, it is possible to detect changes in power levels and to build a box model, consisting of a sequence of rectangles of different heights (power) and widths (time), which is highly adaptable to the real-life working conditions of household appliances. The system was developed and tested using data collected at households in France and the UK (UK-domestic appliance-level electricity (DALE) dataset). The proposed analysis method serves as a basis to be applied to large amounts of data collected by distribution companies with smart meters.

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Section: Resultsmentioning

confidence: 99%

Non-Intrusive Load Monitoring (NILM) for Energy Disaggregation Using Soft Computing Techniques

et al. 2020

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“…Residential buildings. The most commonly used dataset for residential household research is the REDD dataset, featured in 29 articles, e.g., [21,[38][39][40][41][42][43][44][45], followed by the UK-DALE dataset, used in 12 articles, e.g., [39,[46][47][48]. The least used datasets, such as LIFTED, appeared in only one article [49].…”

Section: Data and Data Sourcesmentioning

confidence: 99%

A Scoping Review of Energy Load Disaggregation

Tolnai,

Ma,

Jørgensen

2023

Lecture Notes in Computer Science

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Energy load disaggregation can contribute to balancing power grids by enhancing the effectiveness of demand-side management and promoting electricity-saving behavior through increased consumer awareness. However, the field currently lacks a comprehensive overview. To address this gap, this paper conducts a scoping review of load disaggregation domains, data types, and methods, by assessing 72 full-text journal articles. The findings reveal that domestic electricity consumption is the most researched area, while others, such as industrial load disaggregation, are rarely discussed. The majority of research uses relatively low-frequency data, sampled between 1 and 60 seconds. A wide variety of methods are used, and artificial neural networks are the most common, followed by optimization strategies, Hidden Markov Models, and Graph Signal Processing approaches.

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“…It was decided that the sampling should be at 5, 10, 15, 30 and 60 minutes. Again, several researchers came out with several proposals on the sampling speed [20], [21]. However, in [22] it was mentioned that a desirable sampling frequency depends on the type of load.…”

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confidence: 99%

Development of site-specific non-intrusive load monitoring for maximum demand control

Mukhtaruddin

Hashim

Awang

et al. 2021

IJEECS

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Demand-side load management (DSM) requires greater role-play by end-users. To lower the investment for this load management concept, non-intrusive load management (NILM) was introduced as the solution. However, most of the mathematical techniques used in NILM are complex. This may hinder users from actively take part in the energy management effort. This paper explores the possibilities of applying change point detection techniques with help of differentiation and application of filters. These filters were selected strictly based on site-specific conditions. As part of the NILM implementation, a new and practical technique was developed for this paper. It was found that the developed technique, despite its simplicity it can identify the electrical equipment which added the significant load demand. The performance of the technique was found to be satisfactory as compared to results reported by other researchers.

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Investigation of Deep Learning-based Techniques for Load Disaggregation, Low-Frequency Approach

Cited by 5 publications

References 16 publications

Non-Intrusive Load Monitoring (NILM) for Energy Disaggregation Using Soft Computing Techniques

Non-Intrusive Load Monitoring (NILM) for Energy Disaggregation Using Soft Computing Techniques

A Scoping Review of Energy Load Disaggregation

Development of site-specific non-intrusive load monitoring for maximum demand control

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