Energy management using non-intrusive load monitoring techniques – State-of-the-art and future research directions

Gopinath, R.; Kumar, Mukesh; Joshua, C. Prakash Chandra; Srinivas, K.

doi:10.1016/j.scs.2020.102411

Cited by 147 publications

(83 citation statements)

References 91 publications

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“…It can be seen that efficient energy consumption often depends on effective energy management. For instance, managing a building's energy consumption effectively and efficiently is key to reducing the cost of energy consumption and the risk of future energy shortages in any building [46,47]. Businesses and households monitor and assess their energy consumption with modern data-driven technologies [48].…”

Section: Project Management In An Efficient Energy Consumptionmentioning

confidence: 99%

Effective Management of Energy Consumption during the COVID-19 Pandemic: The Role of ICT Solutions

et al. 2021

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This research tackles effective and functional management practices in energy consumption using advanced technological solutions to mitigate unexpected events. This study specifically focuses on the recent COVID-19 pandemic that led to a reduction in energy consumption and therefore meddled with the project management’s status quo for the energy sources. Moreover, this research analyzes the role of information and communication technology (ICT) solutions in energy management before and after the recent coronavirus pandemic turmoil. This study also examined the role and place of sustainable energy resources in the effective management of energy consumption during the COVID-19 pandemic. The main purpose of this research is to demonstrate how the recent coronavirus pandemic affected energy consumption and energy prices in many countries and how it paved the way for the new ICT in managing energy consumption. This study elaborated a model to show how these changes evolved in selected European Union (EU) Member States. It turns out that adjusted electricity consumption, as well as the average daily spot electricity prices for the baseload power in the EU countries, declined, and this trend is likely to continue in the next years to come. Due to the COVID-19 pandemic, substantial changes in the economy led to reduced office space usage, cutting commuting and business trips, and bringing commercial travel almost to a halt, further shifting the energy balance towards renewable energy sources (RES). Besides, it becomes apparent that ICT solutions used in maintaining and superintending the energy balance are gaining popularity and wide recognition worldwide and will help pave the way towards modern and smart high-renewables grids.

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Section: Project Management In An Efficient Energy Consumptionmentioning

confidence: 99%

Effective Management of Energy Consumption during the COVID-19 Pandemic: The Role of ICT Solutions

et al. 2021

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“…However, some types of appliances operate in more than one state, and others operate continuously between distinct states, making it difficult to model an FSM accurately (Azaza and Wallin 2017). Recent research efforts have been focused on affordable alternatives for FSM or artificial intelligence (Souza et al 2019 Bao et al 2018;Rottondi et al 2019;Gopinath et al 2020).…”

Section: Nilm Background and Psb Implementationmentioning

confidence: 99%

“…Considering the load consumption disaggregation and based on the increasing energyawareness of individual equipment, consumers may adapt consumption behaviours, replace equipment or install management systems focusing on energy/money savings (Baets et al 2017;Mack et al 2019), either on residential, commercial or industrial scenarios (Sadeghianpourhamami et al 2017;Henriet et al 2018;Stankovic et al 2016). Recent NILM studies have been based on different attribute extraction methods, accuracy evaluations, and load disaggregation results ranging from 70% to 98% (Souza et al 2019;Sadeghianpourhamami et al 2017;Esa et al 2016;Wong et al 2013;Abubakar et al 2015;Le and Kim 2018;Aladesanmi and Folly 2015;Klemenjak 2018;Bao et al 2018;Gopinath et al 2020).…”

Section: Introductionmentioning

confidence: 99%

NILM-based approach for energy efficiency assessment of household appliances

et al. 2020

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This paper presents a novel Non-Intrusive Load Monitoring (NILM) approach focusing on the Energy Efficiency (EE) assessment of residential appliances. This approach (NILMEE) is able to identify the individual consumption of several household devices, providing proper information for evaluating energy efficiency and pointing out the operational issues or labelling mismatches of appliances, while recommending better practices for energy usage in specific consumer installations. The proposed approach was developed and evaluated by embedding the NILM engine on an electronic power meter, which performs a microscopic analysis on measured voltages and currents and provides the load disaggregation using the Conservative Power Theory for the feature extraction, K-Nearest Neighbours for the appliance classification, and the Power Signature Blob for the energy disaggregation. The disaggregation algorithm performance evaluation is carried out using NILMTK. Results show that NILM transcends the regular energy usage calculation, serving as a tool that enables the diagnosis of household appliances using the energy efficiency indexes provided by labels and standards.

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“…Various techniques, including signal processing, data mining, shallow learning, and deep learning, have been proposed for ELD in the literature. Readers who are interested in the details may refer to the latest state-of-the-art articles [ 9 , 10 , 11 , 12 ]. Researchers have devoted efforts to enhancing the ELD model from an algorithmic perspective, particularly toward deep learning approaches [ 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

Chui

Gupta

Liu

et al. 2021

Sensors

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Global warming is a leading world issue driving the common social objective of reducing carbon emissions. People have witnessed the melting of ice and abrupt changes in climate. Reducing electricity usage is one possible method of slowing these changes. In recent decades, there have been massive worldwide rollouts of smart meters that automatically capture the total electricity usage of houses and buildings. Electricity load disaggregation (ELD) helps to break down total electricity usage into that of individual appliances. Studies have implemented ELD models based on various artificial intelligence techniques using a single ELD dataset. In this paper, a powerline noise transformation approach based on optimized complete ensemble empirical model decomposition and wavelet packet transform (OCEEMD–WPT) is proposed to merge the ELD datasets. The practical implications are that the method increases the size of training datasets and provides mutual benefits when utilizing datasets collected from other sources (especially from different countries). To reveal the effectiveness of the proposed method, it was compared with CEEMD–WPT (fixed controlled coefficients), standalone CEEMD, standalone WPT, and other existing works. The results show that the proposed approach improves the signal-to-noise ratio (SNR) significantly.

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Energy management using non-intrusive load monitoring techniques – State-of-the-art and future research directions

Cited by 147 publications

References 91 publications

Effective Management of Energy Consumption during the COVID-19 Pandemic: The Role of ICT Solutions

Effective Management of Energy Consumption during the COVID-19 Pandemic: The Role of ICT Solutions

NILM-based approach for energy efficiency assessment of household appliances

Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

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