非侵入式用电负荷监测技术
主要涉及基于有监督多标签组合分类的电器用电模式识别、基于流形学习的半监督多标签分类及用电负荷识别、基于联邦学习的跨用户用电负荷监测等
代表性论文
[1] D. Li and S. Dick, “Residential household non-intrusive load monitoring via graph-based multi-label semi-supervised learning,” IEEE Trans. Smart Grid, vol. 10, no. 4, pp. 4615-4627, 2019. (IF: 10.275，引用次数：74)
[2] D. Li and S. Dick. "Semi-supervised multi-label classification using an extended graph-based manifold regularization." Complex & Intelligent Systems, vol. 8, pp. 1561–1577, 2022. (IF: 6.700)
[3] D. Li and S. Dick, “Non-intrusive load monitoring using multi-label classification methods,” Electrical Engineering, vol. 103, pp. 607-619, 2021.
[4] D. Li and S. Dick, “A graph-based semi-supervised learning approach towards household energy disaggregation,” 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, Italy, pp. 1-7, Jul. 2017.
[5] D. Li and S. Dick, “Whole-house non-intrusive appliance load monitoring via multi-label classification,” 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, pp. 2749-2755, Jul. 2016.
[6] D. Li, K. Sawyer, and S. Dick, “Disaggregating household loads via semi-supervised multi-label classification,” 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS), Redmond, WA, pp. 1-5, Aug. 2015.
