Non-Intrusive Load Monitoring and Classification of Activities of Daily Living Using Residential Smart Meter Data

Devlin, Michael A.; Hayes, Barry

doi:10.1109/tce.2019.2918922

Cited by 114 publications

(73 citation statements)

References 24 publications

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“…Besides the above features, six additional features are extracted: (1) difference power between S and P (DP sp ), (2) difference power between P and Q (DP pq ), (3) difference power between S and Q (DP sq ), (4) average of P , Q and S (P avg ), (5) sine of phase angle (Sin P h), and (6) tangent of phase angle (T an P h), for ENERTALK dataset.…”

Section: B Datasets and Data Preparationmentioning

confidence: 99%

“…According to [1], monitoring energy consumption can save up to 12% of electrical energy with positive impacts on natural resources and reduction of hazardous gas emission. Besides getting detailed insights of the energy usage, NILM is useful for better demand forecasting and tracing behavioral patterns of dwellers [2]. NILM can be realized using three major steps as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…NILM can be realized using three major steps as shown in Fig. 1: (1) Data acquisition which is the collection of data by installing hardware such as smart meters, (2) Feature extraction which is the derivation of features from the collected data, and (3) Learning and inferencing which is the deployment of models such as training machine learning to make prediction. NILM can be exploited for disaggregation using either classification or regression techniques.…”

Section: Introductionmentioning

confidence: 99%

“…A disaggregation model can be trained either as a singletarget [5], [14] or multi-target [15] regression problem, or as a single-label [2], [16]- [18], or multi-label [3], [7], [19]- [22] classification problem. Single-and multi-label classification and multi-class classification were explored in many works in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Target Energy Disaggregation using Convolutional Neural Networks

Ayub¹,

Elsayed²

2020

IJACSA

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Non-Intrusive Load Monitoring (NILM) has become popular for smart meters in recent years due to its low cost installation and maintenance. However, it requires efficient and robust machine learning models to disaggregate the respective electrical appliance energy from the mains. This study investigated NILM in the form of direct point-to-point multiple and single target regression models using convolutional neural networks. Two model architectures have been utilized and compared using five different metrics on two benchmarking datasets (ENERTALK and REDD). The experimental results showed that multi-target disaggregation setting is more complex than single-target disaggregation. For multi-target setting of ENERTALK dataset, the highest individual F1-score is 95.37% and the overall average F1-score is 75.00%. Better results were obtained for the multi-target setting of the other dataset with higher overall average F1-score of 83.32%. Additionally, the robustness and knowledge transfer capability of the models through cross-appliance and cross-domain disaggregation was demonstrated by training for a specific appliance on a specific data, and testing for a different appliance, house and dataset. The proposed models can also disaggregate simultaneous operating appliances with higher F1-scores.

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Section: B Datasets and Data Preparationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Target Energy Disaggregation using Convolutional Neural Networks

Ayub¹,

Elsayed²

2020

IJACSA

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“…Nowadays, worldwide smart electricity meters are widely installed and used in homes and other places. According to research, it is estimated that by the end of 2020, approximately 72% of European homes will have electricity smart meters installed [5,6]. With the advances in smart electricity metering technologies, consumers are aware of their energy consumption patterns over days, weeks, or months.…”

Section: Introductionmentioning

confidence: 99%

Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis

et al. 2020

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The useful planning and operation of the energy system requires a sustainability assessment of the system, in which the load model adopted is the most important factor in sustainability assessment. Having information about energy consumption patterns of the appliances allows consumers to manage their energy consumption efficiently. Non-intrusive load monitoring (NILM) is an effective tool to recognize power consumption patterns from the measured data in meters. In this paper, an unsupervised approach based on dimensionality reduction is applied to identify power consumption patterns of home electrical appliances. This approach can be utilized to classify household activities of daily life using data measured from home electrical smart meters. In the proposed method, the power consumption curves of the electrical appliances, as high-dimensional data, are mapped to a low-dimensional space by preserving the highest data variance via principal component analysis (PCA). In this paper, the reference energy disaggregation dataset (REDD) has been used to verify the proposed method. REDD is related to real-world measurements recorded at low-frequency. The presented results reveal the accuracy and efficiency of the proposed method in comparison to conventional procedures of NILM.

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An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals

et al. 2020

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Nonintrusive load monitoring (NILM) is the de facto technique for extracting device-level power consumption fingerprints at (almost) no cost from only aggregated mains readings. Specifically, there is no need to install an individual meter for each appliance. However, a robust NILM system should incorporate a precise appliance identification module that can effectively discriminate between various devices. In this context, this paper proposes a powerful method to extract accurate power fingerprints for electrical appliance identification. Rather than relying solely on time-domain (TD) analysis, this framework abstracts the phase encoding of the TD description of power signals using a two-dimensional (2D) representation. This allows mapping power trajectories to a novel 2D binary representation space, and then performing a histogramming process after converting binary codes to new decimal representations. This yields the final histogram of 2D phase encoding of power signals, namely, 2D-PEP. An empirical performance evaluation conducted with three realistic power consumption databases collected at distinct resolutions indicates that the proposed 2D-PEP descriptor achieves

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Non-Intrusive Load Monitoring and Classification of Activities of Daily Living Using Residential Smart Meter Data

Cited by 114 publications

References 24 publications

Multi-Target Energy Disaggregation using Convolutional Neural Networks

Multi-Target Energy Disaggregation using Convolutional Neural Networks

Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis

An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals

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