Deep Flexible Transmitter Networks for Non-Intrusive Load Monitoring of Power Distribution Networks

Ma, Chen-Xiao; Yin, Linfei

doi:10.1109/access.2021.3101471

Cited by 5 publications

(4 citation statements)

References 37 publications

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“…The results indicated that using this technique the energy costs and load curtailment can be reduced. In [29], a NILM-based algorithm has been proposed for the monitoring of loads in the power distribution network. This technique consists of a neural network and improves accuracy by 5%.…”

Section: Studymentioning

confidence: 99%

Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring

Shabbir,

Vassiljeva,

Nourollahi Hokmabad

et al. 2024

Electronics

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Non-intrusive load monitoring (NILM) has emerged as a pivotal technology in energy management applications by enabling precise monitoring of individual appliance energy consumption without the requirements of intrusive sensors or smart meters. In this technique, the load disaggregation for the individual device is accrued by the recognition of their current signals by employing machine learning (ML) methods. This research paper conducts a comprehensive comparative analysis of various ML techniques applied to NILM, aiming to identify the most effective methodologies for accurate load disaggregation. The study employs a diverse dataset comprising high-resolution electricity consumption data collected from an Estonian household. The ML algorithms, including deep neural networks based on long short-term memory networks (LSTM), extreme gradient boost (XgBoost), logistic regression (LR), and dynamic time warping with K-nearest neighbor (DTW-KNN) are implemented and evaluated for their performance in load disaggregation. Key evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of each technique in capturing the nuanced energy consumption patterns of diverse appliances. Results indicate that the XgBoost-based model demonstrates superior performance in accurately identifying and disaggregating individual loads from aggregated energy consumption data. Insights derived from this research contribute to the optimization of NILM techniques for real-world applications, facilitating enhanced energy efficiency and informed decision-making in smart grid environments.

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Section: Studymentioning

confidence: 99%

Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring

Shabbir,

Vassiljeva,

Nourollahi Hokmabad

et al. 2024

Electronics

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“…According to the system requirements, each subsystem is designed independently, and they are connected with each other through a series of program interfaces to form a system. The communication program is responsible for the communication between the main Web server and the front-end computer, such as receiving telemetry signals, remote communication signals, alarm signals and coloring signals sent by the front-end computer, and sending remote control signals to the front-end computer by the main Web program [17]. Communication parameter description is used to describe parameter settings of serial communication port, card register information and network address port information.…”

Section: System Architecturementioning

confidence: 99%

Research and development of distribution monitoring configuration software based on network

Zhang,

Xu,

Zhao

et al. 2023

5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)

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In order to solve the problem of network real-time data transmission and the difficult requirements of monitoring system for network data real-time presentation, this paper designs a power monitoring configuration software with Web service function. It is based on B/S architecture, which can expand the equipment primitives for power monitoring applications, and has strong universality and transplant reliability. By using its configuration function, users can quickly generate the power monitoring software system, which can realize the scientific management of power grid monitoring in power enterprises and achieve the purpose of improving work efficiency and automation management level.

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“…Spite that linear state estimation is state-of-the-art, there are many other modern methods for state estimation in today's electric power systems. A state estimation by neural networks is investigated by [9], and [10] proposed a random forest algorithm of state estimation for appliances in office buildings. The Bagging classification algorithm and fuzzy analysis have good performances in state estimation and load monitoring, as reported by [11] and [12].…”

Section: Related Work and Nomenclaturementioning

confidence: 99%

“…From assumptions of the measurement errors, elements of U = (u i,j ) are independent, then the pdf of U is q(U) = i,j q i,j (u i,j ), U = (u i,j ) ∈ R (n+1)×(m+1) , (9) where q i,j (u) is the pdf of u i,j . From assumptions, q 0,i is a Gaussian pdf with known variance and its mean is 0.…”

Section: A Posterior Distribution and Map Estimatormentioning

confidence: 99%

A Belief Propagation Based State Estimator for Semi-Intrusive Load Monitoring System

Duan

Liu

2022

IEEE Access

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For industrial application of load monitoring techniques, it is important to establish high-performance state estimators of low-cost and low frequency smart meters (SM) and sensors in a power system, which can run under resource-constrained computing units. Because household electronic appliances often tap power from fixed sockets, a finite state table for the corresponding sensors is suitable and convenient. However, SM in the main line may have an enormous state table. In this study, we propose a belief propagation (BP) algorithm to calculate the power consumption of electronic appliances in a semi-intrusive load monitoring (SILM) system whose SM and sensors have state tables with sizes varying largely. The novelty of the proposed method lies in a continuous approximation to a large state table and a switching scheme between discrete and continuous parts of the SILM system. With datasets from numerical simulations and a real-world experimental SILM system in a set of high-density school buildings within a secondary distribution network, the proposed BP algorithm is compared with relevant state-of-the-art algorithms. The results show that the proposed algorithm achieves a percentage of error (8%), which outperforms the percentage achieved by the other methods, a linear state estimation of 99%, a hidden Markov model of 21%, and a full-discrete BP algorithm of 11%. In addition, the complexity of the proposed algorithm is the least of all methods, and the proposed algorithm can run by SoC on concentrators.

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Deep Flexible Transmitter Networks for Non-Intrusive Load Monitoring of Power Distribution Networks

Cited by 5 publications

References 37 publications

Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring

Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring

Research and development of distribution monitoring configuration software based on network

A Belief Propagation Based State Estimator for Semi-Intrusive Load Monitoring System

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