Implementation of non-intrusive appliances load monitoring (NIALM) on k-nearest neighbors (k-NN) classifier

Kelati, Amleset; Gaber, Hossam; Plosila, Juha; Tenhunen, Hannu

doi:10.3934/electreng.2020.3.326

Cited by 10 publications

(4 citation statements)

References 10 publications

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“…Each power sequence has several electrical devices that generate power in different periods, and each device is a node of the topology graph. Formally, the topological graph ς is shown in Equation (2).…”

Section: Dynamicgcmmentioning

confidence: 99%

“…In the early stage, to achieve accurate non-intrusive load monitoring (NILM), most of them used simple machine learning methods for optimization analysis, such as k-nearest neighbor (k-NN) [2], support vector machine (SVM) [3], matrix decomposition [4], etc. These methods mainly implement power measurement by sampling within intervals of seconds or minutes.…”

Section: Introductionmentioning

confidence: 99%

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AttG-BDGNets: Attention-Guided Bidirectional Dynamic Graph IndRNN for Non-Intrusive Load Monitoring

Wang

Xin

2023

Information

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Most current non-intrusive load monitoring methods focus on traditional load characteristic analysis and algorithm optimization, lack knowledge of users’ electricity consumption behavior habits, and have poor accuracy. We propose a novel attention-guided bidirectional dynamic graph IndRNN approach. The method first extends sequence or multidimensional data to a topological graph structure. It effectively utilizes the global context by following an adaptive graph topology derived from each set of data content. Then, the bidirectional Graph IndRNN network (Graph IndRNN) encodes the aggregated signals into different graph nodes, which use node information transfer and aggregation based on the entropy measure, power attribute characteristics, and the time-related structural characteristics of the corresponding device signals. The function dynamically incorporates local and global contextual interactions from positive and negative directions to learn the neighboring node information for non-intrusive load decomposition. In addition, using the sequential attention mechanism as a guide while eliminating redundant information facilitates flexible reasoning and establishes good vertex relationships. Finally, we conducted experimental evaluations on multiple open source data, proving that the method has good robustness and accuracy.

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Section: Dynamicgcmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AttG-BDGNets: Attention-Guided Bidirectional Dynamic Graph IndRNN for Non-Intrusive Load Monitoring

Wang

Xin

2023

Information

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“…In contrast to the intrusive approach, non-intrusive load monitoring (NILM) refers to the process by which the overall electricity consumption of a building is identified without intrusion from a single point of measurement, and then the detailed consumption of each device is provided [2,7,8]. The nonintrusive approach is more advantageous than the intrusive one, as it requires only one sensor to be installed, very low maintenance, in addition to being affordable, and non-intrusive to the user [9].…”

Section: Non-intrusive Load Monitoringmentioning

confidence: 99%

Non-Intrusive Load Monitoring (NILM), Interests and Applications

TOKAM,

OURO-DJOBO

2023

J. Nig. Soc. Phys. Sci.

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In developing effective energy management mechanisms, new concepts have been developed to provide new approaches. Non-intrusive load monitoring (NILM) is an approach that was originally developed to allow the occupants of a room to identify the contribution of each appliance to the total electricity consumption of the room through a single point measurement device. The aim is to provide customers with information that will enable them to act as `` ` consum'actors", i.e., people who undertake to change their electricity consumption habits for an objective cause. The progress of artificial intelligence in its various forms (machine learning, big data, internet of things) have greatly contributed to increase the interest of NILM among researchers in different fields. Indeed, some of them are adapting this concept to research areas such as water, transport, health, the environment and agriculture. In this context, applications in these fields have been developed to show the potential and benefits of using this approach. In addition to presenting non-intrusive load monitoring (NILM) in its general framework, this article presents the interests and applications of this approach in various fields.

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“…Considering different appliance characteristics, diverse electric features can be utilized for classification in NILM, such as wavelet-based classification [10] and event-based classification [11]. As to the classification algorithms, the classic k-nearest neighbors was explored in [12] to improve the accuracy and efficiency of NILM. Further on, a support vector machine has been introduced in an early stage [13], enhancing the classification of five nearest neighbor methods.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity

Liu

Shi

Wang

et al. 2021

Sensors

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Acting as a virtual sensor network for household appliance energy use monitoring, non-intrusive load monitoring is emerging as the technical basis for refined electricity analysis as well as home energy management. Aiming for robust and reliable monitoring, the ensemble approach has been expected in load disaggregation, but the obstacles of design difficulty and computational inefficiency still exist. To address this, an ensemble design integrated with multi-heterogeneity is proposed for non-intrusive energy use disaggregation in this paper. Firstly, the idea of utilizing a heterogeneous design is presented, and the corresponding ensemble framework for load disaggregation is established. Then, a sparse coding model is allocated for individual classifiers, and the combined classifier is diversified by introducing different distance and similarity measures without consideration of sparsity, forming mutually heterogeneous classifiers. Lastly, a multiple-evaluations-based decision process is fine-tuned following the interactions of multi-heterogeneous committees, and finally deployed as the decision maker. Through verifications on both a low-voltage network simulator and a field measurement dataset, the proposed approach is demonstrated to be effective in enhancing load disaggregation performance robustly. By appropriately introducing the heterogeneous design into the ensemble approach, load monitoring improvements are observed with reduced computational burden, which stimulates research enthusiasm in investigating valid ensemble strategies for practical non-intrusive load monitoring implementations.

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Implementation of non-intrusive appliances load monitoring (NIALM) on k-nearest neighbors (k-NN) classifier

Cited by 10 publications

References 10 publications

AttG-BDGNets: Attention-Guided Bidirectional Dynamic Graph IndRNN for Non-Intrusive Load Monitoring

AttG-BDGNets: Attention-Guided Bidirectional Dynamic Graph IndRNN for Non-Intrusive Load Monitoring

Non-Intrusive Load Monitoring (NILM), Interests and Applications

An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity

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