Transfer learning for multi-objective non-intrusive load monitoring in smart building
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DOI: 10.1016/j.apenergy.2022.120223
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References listed on IDEAS
- Shi, Xin & Ming, Hao & Shakkottai, Srinivas & Xie, Le & Yao, Jianguo, 2019. "Nonintrusive load monitoring in residential households with low-resolution data," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
- Francesca Paradiso & Federica Paganelli & Dino Giuli & Samuele Capobianco, 2016. "Context-Based Energy Disaggregation in Smart Homes," Future Internet, MDPI, vol. 8(1), pages 1-22, January.
- Rashid, Haroon & Singh, Pushpendra & Stankovic, Vladimir & Stankovic, Lina, 2019. "Can non-intrusive load monitoring be used for identifying an appliance’s anomalous behaviour?," Applied Energy, Elsevier, vol. 238(C), pages 796-805.
- Stankovic, L. & Stankovic, V. & Liao, J. & Wilson, C., 2016. "Measuring the energy intensity of domestic activities from smart meter data," Applied Energy, Elsevier, vol. 183(C), pages 1565-1580.
- Liu, Chao & Akintayo, Adedotun & Jiang, Zhanhong & Henze, Gregor P. & Sarkar, Soumik, 2018. "Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network," Applied Energy, Elsevier, vol. 211(C), pages 1106-1122.
- Patrick Huber & Alberto Calatroni & Andreas Rumsch & Andrew Paice, 2021. "Review on Deep Neural Networks Applied to Low-Frequency NILM," Energies, MDPI, vol. 14(9), pages 1-34, April.
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Cited by:
- Wang, Zhongrui & Xu, Yonghai & He, Sheng & Yuan, Jindou & Yang, Heng & Pan, Mingming, 2023. "A non-intrusive method of industrial load disaggregation based on load operating states and improved grey wolf algorithm," Applied Energy, Elsevier, vol. 351(C).
- Jiangang Lu & Ruifeng Zhao & Bo Liu & Zhiwen Yu & Jinjiang Zhang & Zhanqiang Xu, 2023. "An Overview of Non-Intrusive Load Monitoring Based on V-I Trajectory Signature," Energies, MDPI, vol. 16(2), pages 1-15, January.
- Jiachuan Shi & Dingrui Zhi & Rao Fu, 2023. "Research on a Non-Intrusive Load Recognition Algorithm Based on High-Frequency Signal Decomposition with Improved VI Trajectory and Background Color Coding," Mathematics, MDPI, vol. 12(1), pages 1-20, December.
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Keywords
NILM; Energy disaggregation; Transfer learning; One-to-many model;All these keywords.
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