Low-cost real-time non-intrusive appliance identification and controlling through machine learning algorithm

Khan, Sheharyar; Latif, Ahmad Farhan; Sohaib, Sarmad

doi:10.1109/isce.2018.8408911

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…Commercial Energy management systems and off the shelf solutions are commonly used, including PowerScout 24 [50], eGauge energy monitoring systems [51], EnviR energy aggregator [52], and oscilloscopes [53]. Alternatively, some researchers have developed their own experimental data acquisition systems using devices such as a Raspberry Pi 3 and a Arduino Mega 2560 [54] or the modular open-source phasor measurement unit called OpenPMU [55].…”

Section: Toolsmentioning

confidence: 99%

“…This contributes reducing processing times and data storage required capacity. Conventional algorithms such as decision trees, random forest, support vector machines, k-nearest neighbour and Naïve Bayes belong to the list of supervised NILM methods [54]. In contrast, unsupervised systems provide an automatic learning process of the algorithm without user interaction [86].…”

Section: Load Classificationmentioning

confidence: 99%

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Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks

et al. 2021

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Section: Toolsmentioning

confidence: 99%

Section: Load Classificationmentioning

confidence: 99%

Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks

et al. 2021

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“…In recent years, there are many researches on non-intrusive load monitoring methods at home and abroad. Sheharyar Khan et al [6] realized a low-cost non-invasive load monitoring device based on raspberry pie and machine learning algorithm. However, this scheme does not consider the response speed of the system.…”

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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“…Supervised approaches use labelled data to generate a model, allowing the system to classify the output as known classes. Linear regression, Naïve Bayes, support vector machine (SVM), decision tree (DT), random forest (RF) and k-nearest neighbour (kNN) are one of the most commonly used NILM supervised algorithms [4]. In contrast, non-supervised methods do not require labelling during training, classifying the input into clusters.…”

Section: Introductionmentioning

confidence: 99%

Supervised Non-Intrusive Load Monitoring Algorithm for Electric Vehicle Identification

Jaramillo

Laverty

Rincón

et al. 2020

2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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Transport sector electrification represents an increase in the number of electric vehicles (EV), producing significant variations in the distribution network dynamics. As a result, bidirectional power flow, overload and load unbalances are caused at the low voltage level due to unexpected increased load peaks. Non-intrusive load monitoring (NILM) methods have been developed as a strategy for energy management systems, applied to the customer side producing energy savings. This research presents a NILM methodology based on a low complexity conventional supervised machine learning pipeline. Our approach uses Principal Component Analysis (PCA) and Random Forest (RF) to detect the presence of a charging electric vehicle on the electricity network. By processing low sampling rate active power data, this approach provides a simple but feasible method that can be applied to smart meters. This provides useful data analysis for distribution network operators (DNO) to effectively deal with variability caused by these low carbon loads in the distribution grid. Achieving an overall efficacy of 92.68%, the proposed method can be compared with other state of the art methods developed under higher complexity techniques.

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Low-cost real-time non-intrusive appliance identification and controlling through machine learning algorithm

Cited by 10 publications

References 14 publications

Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks

Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks

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

Supervised Non-Intrusive Load Monitoring Algorithm for Electric Vehicle Identification

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