Load Disaggregation via Pattern Recognition: A Feasibility Study of a Novel Method in Residential Building
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- Kamilaris, Andreas & Kalluri, Balaji & Kondepudi, Sekhar & Kwok Wai, Tham, 2014. "A literature survey on measuring energy usage for miscellaneous electric loads in offices and commercial buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 536-550.
- Cominola, A. & Giuliani, M. & Piga, D. & Castelletti, A. & Rizzoli, A.E., 2017. "A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring," Applied Energy, Elsevier, vol. 185(P1), pages 331-344.
- Liu, Bo & Luan, Wenpeng & Yu, Yixin, 2017. "Dynamic time warping based non-intrusive load transient identification," Applied Energy, Elsevier, vol. 195(C), pages 634-645.
- Aggelos S. Bouhouras & Paschalis A. Gkaidatzis & Konstantinos C. Chatzisavvas & Evangelos Panagiotou & Nikolaos Poulakis & Georgios C. Christoforidis, 2017. "Load Signature Formulation for Non-Intrusive Load Monitoring Based on Current Measurements," Energies, MDPI, vol. 10(4), pages 1-21, April.
- Hosseini, Sayed Saeed & Agbossou, Kodjo & Kelouwani, Sousso & Cardenas, Alben, 2017. "Non-intrusive load monitoring through home energy management systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1266-1274.
- Hsueh-Hsien Chang, 2012. "Non-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on Transient Feature Analyses," Energies, MDPI, vol. 5(11), pages 1-21, November.
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- Wesley Angelino de Souza & Fernando Deluno Garcia & Fernando Pinhabel Marafão & Luiz Carlos Pereira da Silva & Marcelo Godoy Simões, 2019. "Load Disaggregation Using Microscopic Power Features and Pattern Recognition," Energies, MDPI, vol. 12(14), pages 1-18, July.
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Keywords
load disaggregation; pattern recognition; Hidden Markov model; residential building; feasibility study;All these keywords.
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