Home energy efficiency evaluation based on NILM

Jiang, Wenqian; Tang, Litao; Wei, Xingqiu

doi:10.1016/j.procs.2021.02.030

Cited by 4 publications

(5 citation statements)

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“…NILM proves invaluable in deciphering user behavior regarding energy consumption. By disaggregating energy data at the appliance level, NILM provides detailed insights into usage patterns, revealing daily routines, appliance interactions, and trends in energy consumption over time [1], [14], [124], [156], [287], [288]. This level of granularity allows for a deep understanding of when and how users engage with their devices.…”

Section: ) User Behavior Understandingmentioning

confidence: 99%

“…Moreover, NILM offers opportunities for personalized feedback, empowering users with actionable insights to enhance their energy consumption habits [187]. The system's ability to analyze and understand user behavior allows for the development of customized recommendations [289], fostering a culture of energy awareness and efficiency [287]. Researchers also leveraged NILM data for behavioral studies, contributing to a comprehensive understanding of human interactions with energy-consuming devices and facilitating the development of user-centric solutions for smart homes and buildings [4], [290]- [292].…”

Section: ) User Behavior Understandingmentioning

confidence: 99%

“…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%

See 2 more Smart Citations

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%

Section: ) User Behavior Understandingmentioning

confidence: 99%

Section: ) User Behavior Understandingmentioning

confidence: 99%

See 1 more Smart Citation

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

Liu

Wang

You

2019

IEEE Trans. Smart Grid

163

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“…In [51], an EE assessment method has been proposed for home appliances based on NILM. This method uses the disaggregated appliance data to classify electrical home appliances into predefined groups based on the working mechanism and the application of these devices.…”

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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“…Indeed, they highlight notable use cases and outline upcoming research issues, such as how to integrate intelligent meters and cloud computing in NILM to provide new seamless services for smart homes and creative grids. In [ 182 ], the authors stated that home automation and energy management is one of the best approaches to addressing sustainability challenges in the world. Common innovative home power grid scenarios have seen the use of NILM, including flexible load management, targeted energy efficiency improvements, and improved load modeling.…”

Section: Application Of Agents In Sgsmentioning

confidence: 99%

Multi-Agent Systems for Resource Allocation and Scheduling in a Smart Grid

Binyamin

Slama

2022

Sensors

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Multi-Agent Systems (MAS) have been seen as an attractive area of research for civil engineering professionals to subdivide complex issues. Based on the assignment’s history, nearby agents, and objective, the agent intended to take the appropriate action to complete the task. MAS models complex systems, smart grids, and computer networks. MAS has problems with agent coordination, security, and work distribution despite its use. This paper reviews MAS definitions, attributes, applications, issues, and communications. For this reason, MASs have drawn interest from computer science and civil engineering experts to solve complex difficulties by subdividing them into smaller assignments. Agents have individual responsibilities. Each agent selects the best action based on its activity history, interactions with neighbors, and purpose. MAS uses the modeling of complex systems, smart grids, and computer networks. Despite their extensive use, MAS still confronts agent coordination, security, and work distribution challenges. This study examines MAS’s definitions, characteristics, applications, issues, communications, and evaluation, as well as the classification of MAS applications and difficulties, plus research references. This paper should be a helpful resource for MAS researchers and practitioners. MAS in controlling smart grids, including energy management, energy marketing, pricing, energy scheduling, reliability, network security, fault handling capability, agent-to-agent communication, SG-electrical cars, SG-building energy systems, and soft grids, have been examined. More than 100 MAS-based smart grid control publications have been reviewed, categorized, and compiled.

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Home energy efficiency evaluation based on NILM

Cited by 4 publications

References 1 publication

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

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

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

Multi-Agent Systems for Resource Allocation and Scheduling in a Smart Grid

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