The rapid deployment of renewable generations such as photovoltaic (PV) generations brings great challenges to the resiliency of existing power systems. Because PV generations are volatile and typically invisible to the power system operator, estimating the generation and characterizing the uncertainty are in urgent need for operators to make insightful decisions. This paper summarizes our recent results on energy disaggregation at the substation level with Behind-the-Meter solar generation. We formulate the so-called ``partial label'' problem for energy disaggregation at substations, where the aggregate measurements contain the total consumption of multiple loads, and the existence of some loads is unknown. We develop two model-free disaggregation approaches based on deterministic dictionary learning and Bayesian dictionary learning, respectively. Unlike conventional methods which require fully annotated training data of individual loads, our approaches can extract load patterns given partially labeled aggregate data. Therefore, our partial label formulation is more applicable in the real world. Compared with deterministic dictionary learning, the Bayesian dictionary learning-based approach provides the uncertainty measure for the disaggregation results, at the cost of increased computational complexity. All the methods are validated by numerical experiments.
Detecting Activities of Daily Living and Routine Behaviours in Dementia Patients Living Alone Using Smart Meter Load Disaggregation
人口老龄化的出现是一个重大的公共卫生问题。这导致患有进行性神经退行性疾病（如痴呆症）的人数增加。因此，这对卫生和社会保健服务造成的压力意味着提供24小时监测是不可持续的。正在考虑技术干预，但是没有解决方案可以非侵入性地监测痴呆症患者的独立生活需求。因此，许多患者在提供干预和支持之前就达到了危机点。与此同时，病人护理依赖于非正式护理人员对重大行为变化的反馈。然而，并非所有人都有社会支持网络，而且痴呆症护理的早期干预往往被错过。智能电表的推出有可能改变这一点。使用机器学习和信号处理技术，家庭能源供应可以被分解以检测哪些家用电器被打开和关闭。这将使日常生活活动（ADL）得到评估，如饮食，并观察到日常生活的变化，以进行早期干预。主要目的是帮助减少病情恶化，使患者能够在家中呆更长时间。一个支持向量机（SVM）和随机决策森林分类器建模使用的数据从三个测试家庭。然后，在与Mersey Care NHS Foundation Trust合作进行的为期六个月的临床试验中，使用经过训练的模型来监测两名痴呆症患者。在用于电器检测的负载分解的情况下，实现了SVM（AUC=0.86074，Sen=0.756和Spec=0.92838）。而决策森林达到（AUC=0.9429，Sen=0.9634和Spec=0.9634）。还分析了ADL，以识别居住者的行为模式，同时检测日常生活的改变。
Energy Injection Identification enabled Disaggregation with Deep Multi-Task Learning
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；The Hong Kong University of Science（香港科学大学）；Southern University of Science（南方科技大学）；University of Cambridge（剑桥大学）
Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Department of Data Science and AI, Faculty of IT and Monash Energy Institute, Monash University（墨尔本大学数据科学与人工智能系）；Shenzhen Institute for Advanced Study of UESTC（电子科技大学深圳研究院）
The Energy Prediction Smart-Meter Dataset: Analysis of Previous Competitions and Beyond
University of Nottingham（诺丁汉大学）；Universidade de Santiago de Compostela（圣地亚哥德孔波斯特拉大学）；University of Melbourne（墨尔本大学）；University of Granada（格拉纳达大学）；Monash University（莫纳什大学）；IBM Research Europe（IBM欧洲研究院）；E.ON Digital Technology GmbH（E.ON数字技术有限公司）；Honda Research Institute Europe GmbH（本田欧洲研究院有限公司）；National Grid ESO（英国国家电网电力系统运营商）；Coles Group（科尔斯集团）；Amazon（亚马逊）
Automated Machine Learning: A Case Study on Non-Intrusive Appliance Load Monitoring
Department of Computer Science University of Colorado Colorado Springs（计算机科学系，科罗拉多州立大学）；School of Computation, Information, and Technology (CIT) Technical University of Munich（计算、信息与技术学院（CIT），慕尼黑技术大学）；School of CIT Technical University of Munich（计算、信息与技术学院，慕尼黑技术大学）；Software AG Munich, Germany（软件AG，德国慕尼黑）；School of CIT and MDSI Technical University of Munich（计算、信息与技术学院和MDSI，慕尼黑技术大学）
Ask for Any Appliance: A Prompt-Programmable Foundation Model for Non-Intrusive Load Monitoring
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Southern University of Science and Technology（南方科技大学）
KPF-AE-LSTM: A Deep Probabilistic Model for Net-Load Forecasting in High Solar Scenarios
Quantum Energy Partners（量子能源伙伴公司）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；University of Vermont（佛蒙特大学）；Portland General Electric（波特兰通用电气公司）
