NILM in an Industrial Setting: A Load Characterization and Algorithm Evaluation

Holmegaard, Emil; Kjærgaard, Mikkel Baun

doi:10.1109/smartcomp.2016.7501709

Cited by 32 publications

(5 citation statements)

References 13 publications

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“…disaggregation system [32][33][34]. Later works, however, realized the misleading interpretation of this measure (resulting from its bias towards the prevailing class) and proposed precision, recall, and f 1 -score as alternative measures for assessing the disaggregation performance [12,21,35,36].…”

Section: Performance Measuresmentioning

confidence: 99%

On the Feasibility of Generic Deep Disaggregation for Single-Load Extraction

Barsim,

Yang

2018

Preprint

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Recently, and with the growing development of big energy datasets, data-driven learning techniques began to represent a potential solution to the energy disaggregation problem outperforming engineered and hand-crafted models. However, most proposed deep disaggregation models are load-dependent in the sense that either expert knowledge or a hyper-parameter optimization stage is required prior to training and deployment (normally for each load category) even upon acquisition and cleansing of aggregate and sub-metered data. In this paper, we present a feasibility study on the development of a generic disaggregation model based on data-driven learning. Specifically, we present a generic deep disaggregation model capable of achieving state-of-art performance in load monitoring for a variety of load categories. The developed model is evaluated on the publicly available UK-DALE dataset with a moderately low sampling frequency and various domestic loads.

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Section: Performance Measuresmentioning

confidence: 99%

On the Feasibility of Generic Deep Disaggregation for Single-Load Extraction

Barsim,

Yang

2018

Preprint

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“…Popular NILM objectives are load disaggregation and activity identification [6][7][8][9]. In load disaggregation, the target is to identify the specific power consumption of each machine among a group of devices that can be turned on or off individually.…”

Section: Introductionmentioning

confidence: 99%

Detailed Energy Analysis of a Sheet-Metal-Forming Press from Electrical Measurements

Carrillo,

Díaz Dorado,

Cidrás Pidre

et al. 2023

Energies

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This paper presents a methodology that allows for the detection of the state of a sheet-metal-forming press, the parts being produced, their cadence, and the energy demand for each unit produced. For this purpose, only electrical measurements are used. The proposed analysis is conducted at the level of the press subsystems: main motor, transfer module, cushion, and auxiliary systems, and is intended to count, classify, and monitor the production of pressed parts. The power data are collected every 20 ms and show cyclic behavior, which is the basis for the presented methodology. A neural network (NN) based on heuristic rules is developed to estimate the press states. Then, the production period is determined from the power data using a least squares method to obtain normalized harmonic coefficients. These are the basis for a second NN dedicated to identifying the parts in production. The global error in estimating the parts being produced is under 1%. The resulting information could be handy in determining relevant information regarding the press behavior, such as energy per part, which is necessary in order to evaluate the energy performance of the press under different production conditions.

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“…The load monitoring methods of electrical appliances are mainly divided into intrusive and non-intrusive methods. Traditional intrusive load monitoring obtains data by installing power parameter collection devices on each electric appliance [3]. When a large number of electric appliances need to be monitored, more power collection devices are needed, which leads to high cost and inconvenient maintenance and management.…”

Section: Introductionmentioning

confidence: 99%

Electric appliance analysis and identification device based on Kalman filtering algorithm and KNN algorithm

Zou

2022

2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022)

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This paper designs a device for analyzing and identifying electrical appliances. In this system, STM32F103 single chip microcomputer is used as the main control chip, and HT7038 as the core electric quantity detection device. Connect HT7038 with current transformer and voltage transformer to analyze and calculate circuit parameters such as voltage, current and power, so as to determine the types of electrical appliances connected to the circuit. Kalman filter algorithm and KNN algorithm are used to eliminate the noise of voltage and current and improve the accuracy of identification. The test shows that the system can accurately measure the current and active power characteristic parameters of large and small current electrical appliances, identify the working state of electrical appliances, and display the data on the screen in real time. The system has a wide measuring range, high learning ability and recognition ability, fast response speed and high accuracy.

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NILM in an Industrial Setting: A Load Characterization and Algorithm Evaluation

Cited by 32 publications

References 13 publications

On the Feasibility of Generic Deep Disaggregation for Single-Load Extraction

On the Feasibility of Generic Deep Disaggregation for Single-Load Extraction

Detailed Energy Analysis of a Sheet-Metal-Forming Press from Electrical Measurements

Electric appliance analysis and identification device based on Kalman filtering algorithm and KNN algorithm

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