Non-intrusive load monitoring of air conditioning using low-resolution smart meter data

Su, Shi; Yan, Yuting; Lu, Hai; Li, Kangping; Sun, Yujing; Fei, Wang; Liu, Liming; Ren, Hui

doi:10.1109/powercon.2016.7753952

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…Recent advancements in computational capabilities significantly aided the NILM classification methodologies. In this context, numerous techniques are adopted by the research community for the NILM process, which include but are not limited to dynamic time wrapping [28,30], optimization [12,31], machine learning [32][33][34][35][36], neural networks [25,37], and deep learning [38,39]. However, in the context of NILM, supervised machine-learning models are more frequently used as compared to other methodologies.…”

Section: Literature Reviewmentioning

confidence: 99%

Non-Intrusive Load Monitoring of Residential Water-Heating Circuit Using Ensemble Machine Learning Techniques

Rehman

Lie

Vallès³

et al. 2020

Inventions

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The recent advancement in computational capabilities and deployment of smart meters have caused non-intrusive load monitoring to revive itself as one of the promising techniques of energy monitoring. Toward effective energy monitoring, this paper presents a non-invasive load inference approach assisted by feature selection and ensemble machine learning techniques. For evaluation and validation purposes of the proposed approach, one of the major residential load elements having solid potential toward energy efficiency applications, i.e., water heating, is considered. Moreover, to realize the real-life deployment, digital simulations are carried out on low-sampling real-world load measurements: New Zealand GREEN Grid Database. For said purposes, MATLAB and Python (Scikit-Learn) are used as simulation tools. The employed learning models, i.e., standalone and ensemble, are trained on a single household’s load data and later tested rigorously on a set of diverse households’ load data, to validate the generalization capability of the employed models. This paper presents a comprehensive performance evaluation of the presented approach in the context of event detection, feature selection, and learning models. Based on the presented study and corresponding analysis of the results, it is concluded that the proposed approach generalizes well to the unseen testing data and yields promising results in terms of non-invasive load inference.

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Section: Literature Reviewmentioning

confidence: 99%

Non-Intrusive Load Monitoring of Residential Water-Heating Circuit Using Ensemble Machine Learning Techniques

Rehman

Lie

Vallès³

et al. 2020

Inventions

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“…Ref. [17] used a low sampling rate smart meter to collect AC appliance data and built an SVM classification model, and the model can effectively identify the state of AC appliances. Ref.…”

Section: Introductionmentioning

confidence: 99%

Research on Non-Intrusive Load Recognition Method Based on Improved Equilibrium Optimizer and SVM Model

2023

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Non-intrusive load monitoring is the main trend of green energy-saving electricity consumption at present, and load identification is a core part of non-invasive load monitoring. A support vector machine (SVM) is commonly used in load recognition, but there are still some problems in the parameter selection, resulting in a low recognition accuracy. Therefore, an improved equilibrium optimizer (IEO) is proposed to optimize the parameters of the SVM. Firstly, household appliance data are collected, and load features are extracted to build a self-test dataset; and secondly, Bernoulli chaotic mapping, adaptive factors and the Levy flight were introduced to improve the traditional equilibrium optimizer algorithm. The performance of the IEO algorithm is validated on test functions, and the SVM is optimized using the IEO algorithm to establish the IEO-SVM load identification model. Finally, the recognition effect of the IEO-SVM model is verified based on the self-test dataset and the public dataset. The results show that the IEO algorithm has good optimization accuracy and convergence speed on the test function. The IEO-SVM load recognition model achieves an accuracy of 99.428% on the self-test dataset and 100% accuracy on the public dataset, and the classification performance is significantly better than other classification algorithms, which can complete the load recognition task well.

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“…For example, Perez et al [28] proposed an estimation method using k-means and validated their model using an open dataset measured at Pecan Street Inc. in the U.S. [29]. Su et al [30] applied a support vector machine and confirmed the estimation performance using the Pecan Street dataset. Inoue et al [27] adopted averaged one-dependence estimation (AODE), which is a type of Bayesian classifier, and tested the performance of the model based on the experimental data of electricity consumption of fixed-frequency HPs.…”

Section: Introductionmentioning

confidence: 99%

“…Residential ACs or HPs that assist in controlling the speed of the compressor for optimum operation have recently improved energy efficiency significantly compared to conventional fixed-frequency ones, and they are rapidly replacing conventional fixedfrequency ACs and HPs worldwide [30]. For example, inverter-controlled HPs (IHP) had a 100% share in the Japanese residential market by 2020, and the same is expected to increase continuously worldwide.…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Detection of Occupants’ On/Off Behaviours of Residential Air Conditioning

2022

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Understanding occupants’ behaviours (OBs) of heating and cooling use in dwellings is essential for effectively promoting occupants’ behavioural change for energy saving and achieving efficient demand response operation. Thus, intensive research has been conducted on data collection, statistical analysis, and modelling of OBs. However, the majority of smart metres currently deployed worldwide monitor only the total household consumption rather than appliance-level load. Therefore, estimating the turn-on/off state of specific home appliances from the measured household total electricity referred to as non-intrusive load monitoring (NILM), has gained research attention. However, the current NILM methods overlook the specific features of inverter-controlled heat pumps (IHPs) used for space heating/cooling; thus, they are unsuitable for detecting OBs. This study presents a rule-based method for identifying the occupants’ intended operation states of IHPs based on a statistical analysis of load data monitored at 423 dwellings. This method detects the state of IHPs by subtracting the power of sequential-operation appliances other than IHPs from the total household power. Three time-series characteristics, including the durations of power-on/off states and power differences between power-off/on states, were used for this purpose. The performance of the proposed method was validated, indicating an F-score of 0.834.

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Non-intrusive load monitoring of air conditioning using low-resolution smart meter data

Cited by 10 publications

References 9 publications

Non-Intrusive Load Monitoring of Residential Water-Heating Circuit Using Ensemble Machine Learning Techniques

Non-Intrusive Load Monitoring of Residential Water-Heating Circuit Using Ensemble Machine Learning Techniques

Research on Non-Intrusive Load Recognition Method Based on Improved Equilibrium Optimizer and SVM Model

Non-Intrusive Detection of Occupants’ On/Off Behaviours of Residential Air Conditioning

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