eFHMM: Event-Based Factorial Hidden Markov Model for Real-Time Load Disaggregation

Yan, Lei; Tian, Wei; Han, Jiayu; Li, Zuyi

doi:10.1109/tsg.2022.3169398

Cited by 13 publications

(6 citation statements)

References 9 publications

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“…Hidden Markov Models (HMM), often factorial HMMs, are frequently used either independently or in conjunction with other methods. Typically employed in a nonevent-based manner, HMMs can also be utilized based on events to address issues related to other HMM algorithms, as demonstrated in [49]. Wen Fan [54] uses FHMM to model appliance states and an optimization method to solve the disaggregation problem.…”

Section: Related Methodsmentioning

confidence: 99%

“…Less frequently used are GPS [55] and sparse coding [36]. For high-frequency data, optimization [62] and HMM-based methods [49] are the most common. Overall, the diverse range of methods employed across different domains and data frequencies demonstrates the adaptability and versatility of NILM techniques in addressing various load disaggregation problems.…”

Section: Related Methodsmentioning

confidence: 99%

“…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]. Meanwhile, most residential load disaggregation research is conducted on relatively low-frequency data, as seen in 37 articles.…”

Section: Data and Data Sourcesmentioning

confidence: 99%

“…For example, [50] tested their model on data sampled at 15, 30, and 60-second intervals, while [51] used data sampled at 6 and 60-second intervals. Six articles use data between 1 and 15 minutes [16,52,53], five use frequencies higher than 1 second [49,54], two work with hourly data [2,55], and one with 30-minute sampling rate [17].…”

Section: Data and Data Sourcesmentioning

confidence: 99%

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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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Section: Related Methodsmentioning

confidence: 99%

Section: Related Methodsmentioning

confidence: 99%

Section: Data and Data Sourcesmentioning

confidence: 99%

Section: Data and Data Sourcesmentioning

confidence: 99%

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A Scoping Review of Energy Load Disaggregation

Tolnai,

Ma,

Jørgensen

2023

Lecture Notes in Computer Science

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“…The majority of unsupervised learning-based approaches are based on the temporal graphical model, especially, hidden Markov model (HMM) [1]- [3]. Appliances are modeled as HMM and the objective is to estimate the most likely states of appliances.…”

Section: Introductionmentioning

confidence: 99%

An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data

Kim¹,

Yang²,

Lee³

et al. 2022

Preprint

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This paper presents an independent component analysis (ICA) based unsupervised-learning method for heat, ventilation, and air-conditioning (HVAC) load disaggregation using low-resolution (e.g., 15 minutes) smart meter data. We first demonstrate that electricity consumption profiles on mildtemperature days can be used to estimate the non-HVAC base load on hot days. A residual load profile can then be calculated by subtracting the mild-day load profile from the hot-day load profile. The residual load profiles are processed using ICA for HVAC load extraction. An optimization-based algorithm is proposed for post-adjustment of the ICA results, considering two bounding factors for enhancing the robustness of the ICA algorithm. First, we use the hourly HVAC energy bounds computed based on the relationship between HVAC load and temperature to remove unrealistic HVAC load spikes. Second, we exploit the dependency between the daily nocturnal and diurnal loads extracted from historical meter data to smooth the base load profile. Pecan Street data with sub-metered HVAC data were used to test and validate the proposed methods.Simulation results demonstrated that the proposed method is computationally efficient and robust across multiple customers.

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