Standby Power Management of a Smart Home Appliance by Using Energy Saving System With Active Loading Feature Identification

Chen, Ming-Tang; Lin, Chun-Yeon

doi:10.1109/tce.2018.2885034

Cited by 32 publications

(10 citation statements)

References 17 publications

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“…This is important because in the modern world, many household devices remain on standby mode most of the time. According to International Energy Agency (IEA), typical standby power accounts for 3% to 11% of total annual electricity consumption [19].…”

Section: Common Load Monitoring System Stackmentioning

confidence: 99%

Building Appliances Energy Performance Assessment

Jasiunas

Ferreira

Cecílio

2021

Communications in Computer and Information Science

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Conjunto de dados de consumo de energia de eletrodomésticos: Os dados recolhidos podem ser usados para futura investigação científica sobre o consumo de consumo de energia, padrões de uso de energia pelos eletrodomésticos e classificação dos mesmos.Abordagem de computação na borda (Edge Computing): O sistema de monitorização proposto explora o paradigma de computação na borda, onde parte da computação de preparação de dados é executada na borda, libertando recursos da nuvem para cálculos essenciais e que necessitem de mais poder computacional.Classificação precisa de dispositivos em tempo real: Com a proposta desenhada nesta tese, podemos classificar os dispositivos com alta precisão, usando os dados recolhidos pelo sistema de monitorização desenvolvido na tese. A abordagem proposta consegue classificar os dispositivos, que são monitorizados, com baixas taxas de falsos positivos.Para fácil compreensão do trabalho desenvolvido nesta tese, de seguida descreve-se a organização do documento. O Capítulo 1 apresenta o problema do consumo de energia na União Europeia e discute o aumento do consumo da mesma. O capítulo apresenta também os principais objetivos e contribuições do trabalho.No Capítulo 2 revê-se o trabalho relacionado em termos de sistema de monitorização remota, que inclui sensores, microcontroladores, processamento e filtragem de sinal. Por fim, este capítulo revê os trabalhos existentes na literatura relacionados com o problema de classificação de dispositivos usando abordagens de machine learning.No Capítulo 3 discutem-se os requisitos do sistema e o projeto de arquitetura conceitual do sistema. Neste capítulo é proposta uma solução de hardware, bem como, o software e firmware necessários à sua operação. Os algoritmos de machine learning necessários à classificação são também discutidos, em termos de configurações necessárias e adequadas ao problema que queremos resolver nesta tese.O Capítulo 4 representa a implementação de um protótipo que servirá de prova de conceito dos mecanismos discutidos no Capítulo 3. Neste capítulo discute-se também a forma de integração do protótipo na plataforma SATO. Com base na implementação feita, no Capítulo 5 especificam-se um conjunto de testes funcionais que permitem avaliar o desempenho da solução proposta e discutem-se os resultados obtidos a partir desses testes. Por fim, o Capítulo 6 apresenta as conclusões e o trabalho futuro que poderá ser desenvolvido partindo da solução atual.

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Section: Common Load Monitoring System Stackmentioning

confidence: 99%

Building Appliances Energy Performance Assessment

Jasiunas

Ferreira

Cecílio

2021

Communications in Computer and Information Science

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“…The feature extraction module is presented in Figure 2. In Equations (14) and (18), the various currents' and powers' components are presented. For load identification, we limit ourselves to only 10 features, five features of currents and five of powers.…”

Section: Cpc Features Extractionmentioning

confidence: 99%

“…In [13], the authors presented an application of support vector clustering to electrical load pattern classification. The authors of [14] tried to identify the load characteristics of nine appliances in three different operation states (In use, Standby, and Off) by support vector machines as an application of a smart home electric energy saving system. In [15], the authors presented a new method for extracting features in the wavelet domain and used them for classification of washing machines vibration transient signals.…”

Section: Introductionmentioning

confidence: 99%

Harmonic Loads Classification by Means of Currents’ Physical Components

Beck

Machlev

2019

Energies

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Electric load identification and classification for smart grid environment can improve the power service for both consumers and producers. The main concept of electric load identification and classification is to disaggregate various loads and categorize them. In this paper, a new practical method for electric load identification and classification is presented. The method is based on using a power monitor to analyze a real measured current waveform of a grid-connected device. A set number of features is extracted using the currents’ physical components-based power theory decomposition. Using currents’ physical components ensures a constant number of features, which maintains the signal’s characteristics regardless of the harmonic content. These features are used to train a supervised classifier based on two techniques: artificial neural network and nearest neighbor search. The theory is outlined, and experimental results are shown. This paper demonstrates high accuracy performance in identifying an electric load from a designated database. Furthermore, the results show a definite classification of an untrained operation state of a device to the closest trained operation state, for example, the excitation angle of a dimmer. In a comparative study, the method is shown to outperform other state-of-the-art techniques, which are based on harmonic components.

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“…In short, it is the practice of minimizing energy usage without enduring a loss in quality. For many motives, end-users have to support the green energy industry from the first flip on light to the last push on the start button of computers [2,3]. In fact, energy efficiency can enhance the manner buildings consume power in order to diminish detrimental effects on society, economy, and global environment [4].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks

et al. 2020

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Nowadays, analyzing, detecting, and visualizing abnormal power consumption behavior of householders are among the principal challenges in identifying ways to reduce power consumption. This paper introduces a new solution to detect energy consumption anomalies based on extracting micro-moment features using a rule-based model. The latter is used to draw out load characteristics using daily intent-driven moments of user consumption actions. Besides micro-moment features extraction, we also experiment with a deep neural network architecture for efficient abnormality detection and classification. In the following, a novel anomaly visualization technique is introduced that is based on a scatter representation of the micro-moment classes, and hence providing consumers an easy solution to understand their abnormal behavior. Moreover, in order to validate the proposed system, a new energy consumption dataset at appliance level is also designed through a measurement campaign carried out at Qatar University Energy Lab, namely, Qatar University dataset. Experimental results on simulated and real datasets collected at two regions, which have extremely different climate conditions, confirm that the proposed deep micro-moment architecture outperforms other machine learning algorithms and can effectively detect anomalous patterns. For example, 99.58% accuracy and 97.85% F1 score have been achieved under Qatar University dataset. These promising results establish the efficacy of the proposed deep micro-moment solution for detecting abnormal energy consumption, promoting energy efficiency behaviors, and reducing wasted energy.

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Standby Power Management of a Smart Home Appliance by Using Energy Saving System With Active Loading Feature Identification

Cited by 32 publications

References 17 publications

Building Appliances Energy Performance Assessment

Building Appliances Energy Performance Assessment

Harmonic Loads Classification by Means of Currents’ Physical Components

A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks

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