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A Tree-Structured Neural Network Model for Household Energy Breakdown

Published: 13 May 2019 Publication History

Abstract

Residential buildings constitute roughly one-fourth of the total energy use across the globe. Numerous studies have shown that providing an energy breakdown increases residents' awareness of energy use and can help save up to 15% energy. A significant amount of prior work has looked into source-separation techniques collectively called non-intrusive load monitoring (NILM), and most prior NILM research has leveraged high-frequency household aggregate data for energy breakdown. However, in practice most smart meters only sample hourly or once every 15 minutes, and existing NILM techniques show poor performance at such a low sampling rate.
In this paper, we propose a TreeCNN model for energy breakdown on low frequency data. There are three key insights behind the design of our model: i) households consume energy with regular temporal patterns, which can be well captured by filters learned in CNNs; ii) tree structure isolates the pattern learning of each appliance that helps avoid magnitude variance problem, while preserves relationship among appliances; iii) tree structure enables the separation of known appliance from unknown ones, which de-noises the input time series for better appliance-level reconstruction. Our TreeCNN model outperformed seven existing baselines on a public benchmark dataset with lower estimation error and higher accuracy on detecting the active states of appliances.

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Cited By

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  • (2024)Advancing Sustainable IoT Appliance Load Monitoring Through Edge-Enabled Federated Transfer Learning2024 International Conference on Green Energy, Computing and Sustainable Technology (GECOST)10.1109/GECOST60902.2024.10474899(386-391)Online publication date: 17-Jan-2024
  • (2024)TreeCNN and NILMTK Unite: Illuminating Energy Efficiency in Real-World Scenarios2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825584(6884-6893)Online publication date: 15-Dec-2024
  • (2024)Improving the precision of solids velocity measurement in gas-solid fluidized beds with a hybrid machine learning modelChemical Engineering Science10.1016/j.ces.2023.119579285(119579)Online publication date: Mar-2024
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cover image ACM Other conferences
WWW '19: The World Wide Web Conference
May 2019
3620 pages
ISBN:9781450366748
DOI:10.1145/3308558
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2019

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Author Tags

  1. convolutional neural networks
  2. energy breakdown

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WWW '19
WWW '19: The Web Conference
May 13 - 17, 2019
CA, San Francisco, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all
  • (2024)Advancing Sustainable IoT Appliance Load Monitoring Through Edge-Enabled Federated Transfer Learning2024 International Conference on Green Energy, Computing and Sustainable Technology (GECOST)10.1109/GECOST60902.2024.10474899(386-391)Online publication date: 17-Jan-2024
  • (2024)TreeCNN and NILMTK Unite: Illuminating Energy Efficiency in Real-World Scenarios2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825584(6884-6893)Online publication date: 15-Dec-2024
  • (2024)Improving the precision of solids velocity measurement in gas-solid fluidized beds with a hybrid machine learning modelChemical Engineering Science10.1016/j.ces.2023.119579285(119579)Online publication date: Mar-2024
  • (2024)Perfednilm: a practical personalized federated learning-based non-intrusive load monitoringIndustrial Artificial Intelligence10.1007/s44244-024-00016-82:1Online publication date: 16-Apr-2024
  • (2024)Feature Extraction and Energy Disaggregation of Commercial Loads Based on Discrete Wavelet Transform and Sequence-to-Point LearningFrontiers of Energy and Environmental Engineering10.1007/978-981-97-0372-2_17(183-194)Online publication date: 23-Jun-2024
  • (2023)FedNILM: Applying Federated Learning to NILM Applications at the EdgeIEEE Transactions on Green Communications and Networking10.1109/TGCN.2022.31673927:2(857-868)Online publication date: Jun-2023
  • (2022)An Alternative Low-Cost Embedded NILM System for Household Energy Conservation with a Low Sampling RateSymmetry10.3390/sym1402027914:2(279)Online publication date: 29-Jan-2022
  • (2022)More Behind Your Electricity Bill: a Dual-DNN Approach to Non-Intrusive Load Monitoring2022 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics55523.2022.00023(292-299)Online publication date: Aug-2022
  • (2022)An Energy Efficient Smart Metering System Using Edge Computing in LoRa NetworkIEEE Transactions on Sustainable Computing10.1109/TSUSC.2021.30497057:4(786-798)Online publication date: 1-Oct-2022
  • (2021)MC-NILM: A Multi-Chain Disaggregation Method for NILMEnergies10.3390/en1414433114:14(4331)Online publication date: 18-Jul-2021
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