Development of a Non-Intrusive Load Monitoring (NILM) with Unknown Loads using Support Vector Machine

Hernandez, Anjon S.; Ballado, Alejandro H.; Heredia, Aaron Paulo D.

doi:10.1109/i2cacis52118.2021.9495876

Cited by 10 publications

(3 citation statements)

References 7 publications

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“…The addition of the DRO model is achieved by replacing the last layer of the pre-trained model. As a result, the network without DRO addition obtained an F1-score of 0.85 ± 0.061 when using the V-I trajectory map as the input, while the model with DRO addition achieved an F1-score of 0.9058 ± 0.077; SVM (Hernandez A S et al, 2021) achieved an F1score of 0.787 ± 0.091, and KNN obtained an F1-score of 0.77 ± 0.07. When using the Euclidean distance matrix to represent the appliance features, the model with DRO addition achieved an F1-score of 0.87 ± 0.025, which is about 5% higher than the model without DRO addition and 15% higher than the traditional machine learning methods, SVM and KNN.…”

Section: Results On the Lilacdmentioning

confidence: 99%

A Wasserstein-based distributionally robust neural network for non-intrusive load monitoring

et al. 2023

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Non-intrusive load monitoring (NILM) is a technique that uses electrical data analysis to disaggregate the total energy consumption of a building or home into the energy consumption of individual appliances. To address the data uncertainty problem in non-intrusive load monitoring, this paper constructs an ambiguity set to improve the robustness of the model based on the distributionally robust optimization (DRO) framework using the Wasserstein metric. Also, for the hard-to-solve semi-infinite programming problem, a novel and computationally efficient upper-layer approximation is used to transform it into an easily solvable regularization problem. Two different data feature extraction methods are used on two open-source datasets, and the experimental results show that the proposed model has good robustness and performs better in identifying devices with large fluctuations. The improvement is about 6% compared to that of the convolutional neural network model without the addition of distributionally robust optimization. The proposed method supports transfer learning and can be added to the neural network in the form of a single-layer net, avoiding unnecessary training times, while ensuring accuracy.

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Section: Results On the Lilacdmentioning

confidence: 99%

A Wasserstein-based distributionally robust neural network for non-intrusive load monitoring

et al. 2023

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“…Later, more transient features were introduced [12], including active and reactive power changes [13], V-I trajectory [14], current harmonic characteristics [15], and phase noise [16]. Regarding load monitoring algorithms, they can be categorized into combinatorial optimization [17] and pattern recognition [18], including algorithms such as support vector machine (SVM) [19], random forest (RF) [20], K-nearest neighbors (KNN) [21], hidden Markov models (HMM) [22], deep learning [23,24], and others. With the advancement of NILM technology, its applications have expanded from residential homes to commercial buildings [25,26], data centers [27], and smart homes with energy storage, PVs, and electric vehicles [28].…”

Section: Of 22mentioning

confidence: 99%

A Non-Intrusive Identification Approach for Residential Photovoltaic Systems Using Transient Features and TCN with Attention Mechanisms

Ni,

Xia,

et al. 2023

Sustainability

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In order to reduce the negative impact of the large-scale grid connection of residential photovoltaic (PV) equipment on the distribution network, it is of great significance to realize the real-time accurate identification of the grid connection state and its switching of residential PV equipment from the distribution network side. This paper introduces a non-intrusive method for identifying residential PV systems using transient features, leveraging the temporal convolutional network (TCN) model with attention mechanisms. Firstly, the discrimination and redundancy of transient features for residential PV devices are measured using a feature selection method based on the semi-Fisher score and maximal information coefficient (MIC). This enables the construction of a subset of identification features that best characterize the PV devices. Subsequently, a sliding window two-sided cumulative sum (CUSUM) event detection algorithm, incorporating a time threshold, is proposed for the real-time capturing of PV state switching and grid connection behavioral events. This algorithm effectively filters out disturbances caused by the on/off cycles of low-power residential devices and captures the transient time windows of PV behaviors accurately. On this basis, a TCN model with attention mechanisms is proposed to match the discerned event features by assigning varying weights to different types of characteristics, thereby facilitating the precise recognition of a PV grid connection and state-switching events. Finally, the proposed method is validated on a custom-designed non-intrusive experimental platform, demonstrating its precision and real-time efficiency in practical applications.

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“…Bayes classifier has also been proved to be an efficient energy disaggregation approach under non-intrusive strategy [20], and further supports the unsupervised non-parametric modeling with other appliance operation patterns, such as time of usage [21]. Support vector machine (SVM), another typical pattern recognition approach, has been explored for the NILM problem, even for addressing the unknown appliances [22]. Demonstrated to be effective, investigating NILM algorithms based on pattern recognition emerge as a hot topic in academic field, and various approaches including principal component analysis (PCA) [23], non-negative matrix factorization (NMF) [24], and Gaussian mixture model (GMM) [25] are all verified to be contributions.…”

Section: Introduction 1backgroundmentioning

confidence: 96%

Toward Robust Non-Intrusive Load Monitoring via Probability Model Framed Ensemble Method

Liu

Wang

Hong

et al. 2021

Sensors

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As a pivotal technological foundation for smart home implementation, non-intrusive load monitoring is emerging as a widely recognized and popular technology to replace the sensors or sockets networks for the detailed household appliance monitoring. In this paper, a probability model framed ensemble method is proposed for the target of robust appliance monitoring. Firstly, the non-intrusive load disaggregation-oriented ensemble architecture is presented. Then, dictionary learning model is utilized to formulate the individual classifier, while the sparse coding-based approach is capable of providing multiple solutions under greedy mechanism. Furthermore, a fully probabilistic model is established for combined classifier, where the candidate solutions are all labelled with probability scores and evaluated via two-stage decision-making. The proposed method is tested on both low-voltage network simulator platform and field measurement datasets, and the results show that the proposed ensemble method always guarantees an enhancement on the performance of non-intrusive load disaggregation. Besides, the proposed approach shows high flexibility and scalability in classification model selection. Therefore, by initializing the architecture and approach of ensemble method-based NILM, this work plays a pioneer role in using ensemble method to improve the robustness and reliability of non-intrusive appliance monitoring.

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Development of a Non-Intrusive Load Monitoring (NILM) with Unknown Loads using Support Vector Machine

Cited by 10 publications

References 7 publications

A Wasserstein-based distributionally robust neural network for non-intrusive load monitoring

A Wasserstein-based distributionally robust neural network for non-intrusive load monitoring

A Non-Intrusive Identification Approach for Residential Photovoltaic Systems Using Transient Features and TCN with Attention Mechanisms

Toward Robust Non-Intrusive Load Monitoring via Probability Model Framed Ensemble Method

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