A Household Energy Efficiency Index Assessment Method Based on Non-Intrusive Load Monitoring Data

Kong, Xiangyu; Zhu, Shijian; Huo, Xianxu; Li, Shupeng; Li, Ye; Zhang, Siqiong

doi:10.3390/app10113820

Cited by 11 publications

(5 citation statements)

References 38 publications

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“…The objective is to optimize energy use, reduce waste, and achieve a more sustainable and cost-effective operation. In recent years, methods for energy efficiency assessment based on NILM have been developed [287], [293]- [295]. These studies have demonstrated that by combining the analysis results of energy efficiency indicators, household users can identify weaknesses in their energy efficiency and improve overall electricity efficiency by adjusting their energy-use patterns.…”

Section: ) User Behavior Understandingmentioning

confidence: 99%

Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Liu

Wang

You

2019

IEEE Trans. Smart Grid

163

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Non-Intrusive Load Monitoring (NILM) is pivotal in today's energy landscape, offering vital solutions for energy conservation and efficient management. Its growing importance in enhancing energy savings and understanding consumer behavior makes it a pivotal technology for addressing global energy challenges. This paper delivers an in-depth review of NILM, highlighting its critical role in smart homes and smart grids. The significant contributions of this study are threefold: Firstly, it compiles a comprehensive global dataset table, providing a valuable tool for researchers and engineers to select appropriate datasets for their NILM studies. Secondly, it categorizes NILM approaches, simplifying the understanding of various algorithms by focusing on technologies, label data requirements, feature usage, and monitoring states. Lastly, by identifying gaps in current NILM research, this work sets a clear direction for future studies, discussing potential areas of innovation.

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Section: ) User Behavior Understandingmentioning

confidence: 99%

Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Liu

Wang

You

2019

IEEE Trans. Smart Grid

163

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“…Non-invasive load monitoring (NILM) technologies have been employed to collect more detailed data on the utilisation of certain appliances [125,126]. Data collection capabilities of the Internet of Things (IoT) technology have been significantly improved in recent research.…”

Section: Data and Measurement Techniques For The Prediction Of Electr...mentioning

confidence: 99%

Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future Directions

Kachalla,

Ghiaus

2024

Energies

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Accurate and efficient prediction of electric water boiler (EWB) energy consumption is significant for energy management, effective demand response, cost minimisation, and robust control strategies. Adequate tracking and prediction of user behaviour can enhance renewable energy mini-grid (REMD) management. Fulfilling these demands for predicting the energy consumption of electric water boilers (EWB) would facilitate the establishment of a new framework that can enhance precise predictions of energy consumption trends for energy efficiency and demand management, which necessitates this state-of-the-art review. This article first reviews the factors influencing the prediction of energy consumption of electric water boilers (EWB); subsequently, it conducts a critical review of the current approaches and methods for predicting electric water boiler (EWB) energy consumption for residential building applications; after that, the performance evaluation methods are discussed. Finally, research gaps are ascertained, and recommendations for future work are summarised.

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“…The disaggregated information of home appliances obtained by NILM is a great tool to help in the evaluation of the EE in buildings. In [47], a NILM-based assessment method is proposed to evaluate the EE of households. To enhance the accuracy, this study introduced an EE assessment index of residents using the analysis of both electrical and non-electrical factors that could affect energy efficiency.…”

Section: Energy Efficiency Assessment Of Hvacs Using Nilmmentioning

confidence: 99%

Fault Detection and Efficiency Assessment for HVAC Systems Using Non-Intrusive Load Monitoring: A Review

2022

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Heat, ventilation, and air conditioning (HVAC) systems are some of the most energy-intensive equipment in buildings and their faulty or inefficient operation can significantly increase energy waste. Non-Intrusive Load Monitoring (NILM), which is a software-based tool, has been a popular research area over the last few decades. NILM can play an important role in providing future energy efficiency feedback and developing fault detection and diagnosis (FDD) tools in smart buildings. Therefore, the review of NILM-based methods for FDD and the energy efficiency (EE) assessment of HVACs can be beneficial for users as well as buildings and facilities operators. To the best of the authors’ knowledge, this paper is the first review paper on the application of NILM techniques in these areas and highlights their effectiveness and limitations. This review shows that even though NILM could be successfully implemented for FDD and the EE evaluation of HVACs, and enhance the performance of these techniques, there are many research opportunities to improve or develop NILM-based FDD methods to deal with real-world challenges. These challenges and future research works are also discussed in-depth.

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A Household Energy Efficiency Index Assessment Method Based on Non-Intrusive Load Monitoring Data

Cited by 11 publications

References 38 publications

Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future Directions

Fault Detection and Efficiency Assessment for HVAC Systems Using Non-Intrusive Load Monitoring: A Review

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