Is disaggregation the holy grail of energy efficiency? The case of electricity

Cited by (593)

Items 301–400 of 593

1. Pre-trained non-intrusive load monitoring model for recognizing activity of daily living
   Gyubaek Kim, Sanghyun Park · 2022 · Applied Intelligence
2. GT-NILM: A Generative, Transferable Non-Intrusive Load Monitoring System Based on Conditional Diffusion Models and Convolutional Neural Networks
   Bohan Zhang, Fengji Luo, Yu Ting He, Gianluca Ranzi · 2025 · IEEE Transactions on Consumer Electronics
3. Reproducible non-intrusive load monitoring: A survey of open-source methods and practical challenges
   Zhongzong Yan, Matteo Nardello, Davide Brunelli, He Wen · 2025 · Renewable and Sustainable Energy Reviews
4. Energy disaggregation for SMEs using recurrence quantification analysis
   2018 · CentAUR (University of Reading)
5. The Use of Real Energy Consumption Data in Characterising Residential Energy Demand with an Inventory of UK Datasets
   Lesley Thomson, David Paul Jenkins · 2023 · Energies
6. Load Monitoring and Appliance Recognition Using an Inexpensive, Low-Frequency, Data-to-Image, Neural Network, and Network Mobility Approach for Domestic IoT Systems
   Peppino Fazio, Miralem Mehić, Miroslav Vozn̆ák · 2023 · IEEE Internet of Things Journal
7. On modeling the sporadic impulsive noise rate within in-home power line networks
   Salvatore D’Alessandro, Marco De Piante, Andrea M. Tonello · 2015 · IEEE International Symposium on Power Line Communications and Its Applications (ISPLC)
8. Sparse-coding-based household clustering for demand response services
   Shintaro Ikeda, Hiroaki Nishi · 2016 · IEEE International Symposium on Industrial Electronics (ISIE)
9. CLEAR — A circuit level electric appliance radar for the electric cabinet
   Anwar Ul Haq, Thomas Kriechbaumer, Matthias Kahl, Hans‐Arno Jacobsen · 2017 · IEEE International Conference on Industrial Technology (ICIT)
10. Label Correction and Event Detection for Electricity Disaggregation
    Mark Valovage, Maria L. Gini · 2017 · Adaptive Agents and Multi-Agents Systems
11. Energy disaggregation for identifying anomalous appliance
    Haroon Rashid, Pushpendra Singh · 2017 · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys)
12. A combination of SOM-based operating time estimation and simplified disaggregation for SME buildings using hourly energy consumption data
    Hidenori Komatsu, Osamu Kimura · 2019 · Energy and Buildings
13. Impact of nonintrusive load monitoring on CO2 emissions in Malaysia
    Keh-Kim Kee, Yun Seng Lim, Jianhui Wong, Kein Huat Chua · 2021 · Bulletin of Electrical Engineering and Informatics
14. Non-intrusive load monitoring system for similar loads identification using feature mapping and deep learning techniques
    Mukesh Kumar, R Gopinath, P. Harikrishna, Kota Srinivas · 2021 · Measurement Science and Technology
15. Load Identification and Classification of Activities of Daily Living using Residential Smart Meter Data
    Michael A. Devlin, Barry Patrick Hayes · 2019
16. Where Are They Now?
    Stephen Snow, Stephen Viller, Mashhuda Glencross, Neil Horrocks · 2019 · Australian Conference on Human-Computer Interaction (OzCHI)
17. Non-Intrusive Load Monitoring of Residential Water-Heating Circuit Using Ensemble Machine Learning Techniques
    Attique Ur Rehman, Tek Tjing Lie, Brice Vallès, Shafiqur Rahman Tito · 2020 · Inventions
18. Advances in Machine-Learning Based Disaggregation of Building Heating Loads: A Review
    Synne Krekling Lien, Behzad Najafi, Jayaprakash Rajasekharan · 2023 · Lecture notes in computer science
19. Inferring the behavior of distributed energy resources with online learning
    Gregory S. Ledva, Laura Balzano, Johanna L. Mathieu · 2015 · Allerton Conference on Communication, Control, and Computing
20. Energy Disaggregation and the Utility-Privacy Tradeoff
    Roy Dong, Lillian J. Ratliff · 2017 · Elsevier eBooks
21. Energy disaggregation meets heating control
    Stephan Spiegel, Şahin Albayrak · 2014 · ACM Symposium on Applied Computing
22. A Grand Challenge-based Framework for Contextual Learning in Engineering: Impact on Student Outcomes and Motivation
    Lisa G. Huettel, Michael R. Gustafson, Joseph C. Nadeau, David E. Schaad, Michael M. Barger, Lisa Linnenbrink‐Garcia · 2015 · ASEE Annual Conference & Exposition
23. Economic Model Predictive and Feedback Control of a Smart Grid Prosumer Node
    Francesco Liberati, Alessandro Di Giorgio · 2017 · Energies
24. Design and implementation of a low-cost arduino-based high-frequency AC waveform meter board for the raspberry pi
    Matías Quintana, Henning Lange, Mario E. Berges · 2017 · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys)
25. How behavioral and geographic heterogeneity affects economic and environmental benefits of efficient appliances
    Ashok Sekar, Eric Williams, Eric Hittinger, Roger B. Chen · 2018 · Energy Policy
26. SCENe Things: IoT-based Monitoring of a Community Energy Scheme
    Rob Shipman, Mark Gillott · 2019 · Future Cities and Environment
27. A Framework to Generate and Label Datasets for Non-Intrusive Load Monitoring
    Benjamin Völker, Marc Pfeifer, Philipp Scholl, Bernd Becker · 2020 · Energies
28. NILM Application for Real Time Monitoring of Appliances Energy Consumption Used
    Krzysztof Dowalla, Piotr Bilski, Robert Łukaszewski, Augustyn Wójcik, Ryszard Kowalik · 2021 · IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)
29. A Lightweight Neural Network for Energy Disaggregation Employing Depthwise Separable Convolution
    Ruiqi Zhang, Wenpeng Luan, Bo Liu, Mingjun Zhong · 2021 · IEEE Sustainable Power and Energy Conference (iSPEC)
30. Generation of meaningful synthetic sensor data — Evaluated with a reliable transferability methodology
    Michael Meiser, Benjamin Duppe, Ingo Zinnikus · 2023 · Energy and AI
31. Personalized Energy Reduction Cyber-physical System (PERCS): A Gamified End-User Platform for Energy Efficiency and Demand Response
    Nicole D. Sintov, Michael D. Orosz, P. Wesley Schultz · 2015 · Lecture notes in computer science
32. Estimating human interactions with electrical appliances for activity-based energy savings recommendations
    Hông-Ân Cao, Tri Kurniawan Wijaya, Karl Aberer, Nuno Nunes · 2016 · IEEE International Conference on Big Data (BigData)
33. Electrical Appliance Classification using Deep Convolutional Neural Networks on High Frequency Current Measurements
    Daniel Jorde, Thomas Kriechbaumer, Hans‐Arno Jacobsen · 2018 · IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
34. Low-Rate Non-Intrusive Appliance Load Monitoring Based on Graph Signal Processing
    Bing Zhang, Shengjie Zhao, Qingjiang Shi, Rongqing Zhang · 2019 · International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)
35. Device-Free User Activity Detection using Non-Intrusive Load Monitoring
    Andreas Reinhardt, Christoph Klemenjak · 2020
36. WITHDRAWN: Non-intrusive load monitoring technique using deep neural networks for energy disaggregation
    Subash Ranjan Kabat, D. Jayashree, K.G.S. Venkatesan, Haritha Budaraju Venkata · 2021 · Materials Today Proceedings · Retracted
37. XNILMBoost: Explainability-informed load disaggregation training enhancement using attribution priors
    Djordje Batic, Vladimir Stanković, Lina Stanković · 2024 · Engineering Applications of Artificial Intelligence
38. Thermostat metrics derived from HVAC cycling data for targeted utility efficiency programs
    Sylvia Smullin · 2016 · Energy and Buildings
39. Experiment and simulation of sliding mode triboelectric energy harvester based on slider‐crank mechanism
    Satish Kumar, Diwakar Singh, Rajeev Kumar, Satish Chandra Jain · 2023 · Environmental Progress & Sustainable Energy
40. HeartDIS: A Generalizable End-to-End Energy Disaggregation Pipeline
    Ilias Dimitriadis, Nikolaos Virtsionis Gkalinikis, Nikolaos Gkiouzelis, Athena I. Vakali, Christos L. Athanasiadis, Costas G. Baslis · 2023 · Energies
41. Implementing a CNN in FPGA Programmable Logic for NILM Application
    Miguel Tapiador, Laura de Diego-Otón, Álvaro Hernández, Rubén Nieto · 2023 · Conference on Design of Circuits and Integrated Systems
42. Development and Application of an Open Power Meter Suitable for NILM
    Carlos Rodríguez-Navarro, Francisco Portillo, Fernando Martínez, Francisco Rogelio Manzano-Agugliaro, Alfredo Alcayde · 2023 · Inventions
43. Robust dictionary learning: Application to signal disaggregation
    Angshul Majumdar, Rabab Kreidieh Ward · 2016 · IEEE International Conference on Acoustics Speech and Signal Processing
44. A study of the communication needs in micro-grid systems
    Christos Mavrokefalidis, Dimitris Ampeliotis, Kostas Berberidis · 2017 · URSI General Assembly and Scientific Symposium
45. IoT Based Automation of Electricity Consumption in Smarthomes
    Mohanapriya D, R. Reshma, D. Shofia Priyadharshini, Swathi Vinod · 2019 · International Conference on System, Computation, Automation and Networking (ICSCAN)
46. A new graph learning-based signal processing approach for non-intrusive load disaggregation with active power measurements
    Mingyue Zhai · 2019 · Neural Computing and Applications
47. Event Detection for Energy Consumption Monitoring
    Daniel Jorde, Hans‐Arno Jacobsen · 2020 · IEEE Transactions on Sustainable Computing
48. A Practical Load Disaggregation Approach for Monitoring Industrial Users Demand with Limited Data Availability
    Sara Tavakoli, Kaveh Rajab Khalilpour · 2021 · Energies
49. MCIR-CNN: Multichannel Imaging-Based Nonintrusive Load Identification With Rényi Entropy Window for Improved Sensor Data Processing
    Yang Xu, Qingshan Xu, Yongbiao Yang · 2023 · IEEE Sensors Journal
50. Appliance classification using energy disaggregation in smart homes
    Sourodeep Bhattacharjee, Anirudh Kumar, Joydeb Roychowdhury · 2014 · International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC)
51. Nonintrusive load identification using extreme learning machine and TT-transform
    Khairuddin Khalid, Azah Mohamed, Ramizi Mohamed, Hussain Shareef · 2016 · International Conference on Advances in Electrical, Electronic and Systems Engineering
52. Assessing the flexibility potential of the residential load in smart electricity grids — A data-driven approach
    Delaram Azari, Shahab Shariat Torbaghan, Hans J. Cappon, Madeleine Gibescu, Karel J. Keesman, Huub H. M. Rijnaarts · 2017 · International Conference on the European Energy Market (EEM)
53. Non-intrusive load disaggregation via a fully convolutional neural network: improving the accuracy on unseen household
    Luca Massidda, Marino Marrocu, Simone Manca · 2020 · IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES)
54. Office Appliances Identification and Monitoring using Deep Leaning based Energy Disaggregation for Smart Buildings
    Mohammed Taha O. El Astal, M. Kalloub, Ammar M. Abu‐Hudrouss, Georg Frey · 2020 · Annual Conference of the IEEE Industrial Electronics Society (IECON)
55. Improving NILM by Combining Sensor Data and Linear Programming
    Nicolas Roux, Baptiste Vrigneau, Olivier Sentieys · 2019 · IEEE Sensors Applications Symposium (SAS)
56. Combining several distinct electrical features to enhance nonintrusive load monitoring
    Timo Bernard, Daniel Wohland, Julian Klaassen, Gerd vom Bögel · 2015 · International Conference on Smart Grid and Clean Energy Technologies
57. Enhancing smart home appliance recognition with wavelet and scalogram analysis using data augmentation
    Jose L. Salazar González, José María Luna-Romera, Manuel Carranza-García, Juan A. Álvarez-García, Luis Miguel Soria Morillo · 2024 · Integrated Computer-Aided Engineering
58. Appliance characterization based on spectral components analysis
    Marinel Costel Temneanu · 2014 · International Conference and Exposition on Electrical and Power Engineering (EPE)
59. Estimating human interactions with electrical appliances for activity-based energy savings recommendations
    Hông-Ân Cao, Tri Kurniawan Wijaya, Karl Aberer · 2014 · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys)
60. AutoPlug: An automated metadata service for smart outlets
    Lurdh Pradeep Reddy Ambati, David Irwin · 2016 · International Green and Sustainable Computing Conference
61. A Study on Fundamental Waveform Shapes in Microscopic Electrical Load Signatures
    Raneen Younis, Andreas Reinhardt · 2020 · Energies
62. Comparative Feedbacks under Incomplete Information
    Nicolas Astier · 2016 · HAL
63. Efficient Deep Learning Techniques for Water Disaggregation
    Nikolaos Virtsionis Gkalinikis, Dimitris Vrakas · 2022 · International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)
64. A Design and Comparative Analysis of a Home Energy Disaggregation System Based on a Multi-Target Learning Framework
    Bundit Buddhahai, Suratsavadee Koonlaboon Korkua, Pattana Rakkwamsuk, Stephen Makonin · 2023 · Buildings
65. Synergizing disparate component-level energy resources into a single whole building tool to support energy conservation action in small commercial buildings
    Kevin J. Ketchman, Kristen Parrish, Vikas Khanna, Melissa Marie Bilec · 2018 · Energy and Buildings
66. Data driven fault tolerant thermal management of data centers
    Masoud Kheradmandi, Douglas G. Down · 2020 · 2012 International Conference on Computing, Networking and Communications (ICNC)
67. Automated Non-Intrusive Load Monitoring System Using Stacked Neural Networks and Numerical Integration
    Suelene de Jesus do Carmo, ADRIANA ROSA GARCEZ CASTRO · 2020 · IEEE Access
68. Non-intrusive Load Monitoring for Residential Customers Using Adaptive-Neuro Fuzzy Interface System and Fine Tree Classifier
    Muhammad Zaigham Abbas, Intisar Ali Sajjad, Rehan Liaqat, Muhammad Abdullah, Muhammad Athar Shah, Muhammad Faisal Nadeem · 2020 · IEEE International Multitopic Conference (INMIC)
69. Post-metering value-added services for low voltage electricity users: Lessons learned from the Italian experience of CHAIN 2
    Daniele Serra, Daniele Mardero, Luca Di Stefano, Samuele Grillo · 2021 · Applied Energy
70. Home Occupancy Classification Using Machine Learning Techniques along with Feature Selection
    Abdullah-Al Nahid, Niloy Sikder, Mahmudul Hasan Abid, Rafia Nishat Toma, Iffat Ara Talin, Lasker Ershad Ali · 2022 · International Journal of Engineering and Manufacturing
71. Machine learning approach for non-intrusive load monitoring in smart grids: new deep learning method based on long short-term memory and convolutional neural networks
    Sobhan Naderian · 2022 · Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)
72. Single-channel compressive sampling of electrical data for non-intrusive load monitoring
    Michelle Clark, Lutz Lampe · 2015 · IEEE Global Conference on Signal and Information Processing
73. Accuracy comparison of low-cost energy meters for home electrical consumption assessment
    Mohamed Nait Meziane, Philippe Ravier, Guy Lamarque, Jean-Charles Le Bunetel, Yves Raingeaud · 2016 · IEEE International Conference on Environment and Electrical Engineering and IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
74. State identification of home appliance with transient features in residential buildings
    Lei Yan, Runnan Xu, Mehrdad Sheikholeslami, Yang Li, Zuyi Li · 2022 · Frontiers in Energy
75. GraphNILM: A Graph Neural Network for Energy Disaggregation
    Rui Shang, Siji Chen, Zhiqian Chen, Chang‐Tien Lu · 2024 · Lecture notes in computer science
76. Consumption awareness for energy savings: NIALM algorithm efficiency evaluation
    Giuseppe Marco Tina, Valeria Amenta · 2014 · International Renewable Energy Congress
77. Detecting Activities of Daily Living from Low Frequency Power Consumption Data
    Yujin Tang, Chihiro Ono · 2016 · EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous)
78. Enabling demand side integration - assessment of appropriate information and communication technology infrastructures, their costs and possible impacts on the electricity system
    Martin Steurer, Michael Z. Miller, Ulrich Fahl, Kai Hufendiek · 2015
79. Electric Load Profile Non-intrusive Analysis to Guide Energy Efficient Behavior for Residential Clients
    Etta Grover-Silva, Elena Magliaro, Johanna Le Conte · 2019 · IEEE PES Innovative Smart Grid Technologies Europe
80. A Comprehensive Survey for Non-Intrusive Load Monitoring
    Efe İsa Tezde, ERAY YILDIZ · 2022 · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES
81. Energy Hogs and Misers: Magnitude and Variability of Individuals’ Household Electricity Consumption
    Claudia Bustamante, Stephen Bird, Lisa Legault, Susan E. Powers · 2023 · Sustainability
82. Leveraging the Integration of IoT Devices for Energy Optimization in Office Blocks
    Tendai Chada, Chrispen Mafirabadza · 2024 · International Journal of Innovative Science and Research Technology (IJISRT)
83. RHEA: Residential Home Energy Advisor
    Nikolaos Virtsionis Gkalinikis, Christoforos Nalmpantis, Dimitris Vrakas, Chatzigeorgiou Spyridon, Christos L. Athanasiadis, Dimitrios I. Doukas · 2025 · International Conference on Smart and Sustainable Technologies (SpliTech)
84. Optimization of In-House Energy Demand
    Stephan Spiegel · 2015 · Advances in computer vision and pattern recognition
85. Leveraging smart meters for residential energy disaggregation
    Abiodun Iwayemi, Chi Zhou · 2014 · IEEE Power Engineering Society General Meeting
86. Model-Free Iterative Temporal Appliance Discovery for Unsupervised Electricity Disaggregation
    Mark Valovage, Akshay Shekhawat, Maria L. Gini · 2018 · Proceedings of the AAAI Conference on Artificial Intelligence
87. Algorithms for Energy Justice
    Johanna L. Mathieu · 2023 · Women in engineering and science
88. Power system load data models and disaggregation based on sparse approximations
    Rahul Sinha, S. Spoorthy, Prerna Khurana, M Girish Chandra · 2016 · IEEE International Conference on Industrial Informatics (INDIN)
89. Self-Supervised Learning of Appliance Usage
    Chen-Yu Hsu, Abbas Zeitoun, Guang-He Lee, Dina Katabi, Tommi Jaakkola · 2020 · International Conference on Learning Representations
90. A Versatile High Frequency Electricity Monitoring Framework for Our Future Connected Home
    Benjamin Völker, Marc Pfeifer, Philipp Scholl, Bernd Becker · 2020 · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
91. Privacy Aware Demand Response and Smart Metering
    Abdalkarim Awad, Peter Bazan, Reinhard German · 2015 · IEEE Vehicular Technology Conference
92. Non-Intrusive Appliance Identification for Energy Disaggregation of Indian Households–An Use Case for Energy Informatics
    Anirudh Kumar, Partha S. Bhattacharjee · 2018 · IEEE International Symposium on Smart Electronic Systems (iSES)
93. People, Demand and Governance in Future Energy Systems
    RD Hoggett · 2017 · Open Research Exeter (University of Exeter)
94. CREAM, a component level coffeemaker electrical activity measurement dataset
    Daniel Jorde, Thomas Kriechbaumer, Tim Berger, Stefan Zitzlsperger, Hans‐Arno Jacobsen · 2020 · Scientific Data
95. The Electricity Load Prediction Model for Residential Buildings: A Critical Review of Output Types, Prediction Methods and Driving Factors
    Zhenjing Wu, Min Qi, Weiling Zhang, Xudong Zhang, Qiang Yang, Wenyuan Zhao, Bin Yang, Zhihan Lv, Faming Wang, Zhichao Wang · 2025 · Buildings
96. Smart Metering Applications
    Nikolaos Efkarpidis, Martin Geidl, Holger Wache, Marco Peter, Marc Adam · 2022 · Lecture notes in energy
97. Analysis of regional differences in green energy efficiency in countries along “the Belt and Road” Initiative zone—Based on super efficiency DEA model and Malmquist index method
    Sumin Li, Meng Wu, Meizhe Song · 2023 · Frontiers in Energy Research
98. Energy Disaggregation with NILM on a Raspberry Pi with Smart-Metering Extension
    Johannes Winkler, Hafsa Bousbiat, Stefan Jost, Wilfried Elmenreich · 2023 · International Conference on Power Systems and Electrical Technology (PSET)
99. Supervised energy disaggregation using dictionary — based modelling of appliance states
    Christos Mavrokefalidis, Dimitris Ampeliotis, Evangelos Vlachos, Kostas Berberidis, Emmanouel Varvarigos · 2016 · IEEE PES Innovative Smart Grid Technologies Europe
100. Measurements for remote identification of electrical equipment
     Richard O. Lane, T.A. Badran, Gillian F. Marshall · 2017 · Electronics Letters