This paper addresses the energy disaggregation problem, i.e. decomposing the electricity signal of a whole home to its operating devices. First, we cast the problem as a dictionary learning (DL) problem where the key electricity patterns representing consumption behaviors are extracted for each device and stored in a dictionary matrix. The electricity signal of each device is then modeled by a linear combination of such patterns with sparse coefficients that determine the contribution of each device in the total electricity. Although popular, the classic DL approach is prone to high error in real-world applications including energy disaggregation, as it merely finds linear dictionaries. Moreover, this method lacks a recurrent structure; thus, it is unable to leverage the temporal structure of energy signals. Motivated by such shortcomings, we propose a novel optimization program where the dictionary and its sparse coefficients are optimized simultaneously with a deep neural model extracting powerful nonlinear features from the energy signals. A long short-term memory auto-encoder (LSTM-AE) is proposed with tunable time dependent states to capture the temporal behavior of energy signals for each device. We learn the dictionary in the space of temporal features captured by the LSTM-AE rather than the original space of the energy signals; hence, in contrast to the traditional DL, here, a nonlinear dictionary is learned using powerful temporal features extracted from our deep model. Real experiments on the publicly available Reference Energy Disaggregation Dataset (REDD) show significant improvement compared to the state-of-the-art methodologies in terms of the disaggregation accuracy and F-score metrics.
Season-Independent PV Disaggregation Using Multi-Scale Net Load Temporal Feature Extraction and Weather Factor Fusion
School of Computer Science and Technology University of Electronic Science and Technology of China（电子科学与技术大学计算机科学与技术学院）；Department of Data Science and AI Faculty of Information Technology Monash University（信息科技学院数据科学与人工智能系）；Monash Energy Institute Monash University（莫纳什大学能源研究所）；Shenzhen Institute for Advanced Study Shenzhen, China（深圳先进研究 institute）
Temporal Disaggregation of the Cumulative Grass Growth
Inria Centre de Lyon（里昂法国国家信息与自动化研究所中心）；Université Rennes 1（雷恩第一大学）；ENSAI（法国国立统计与信息分析学院）；IRISA（法国信息与随机系统研究所）；INRAE（法国国家农业食品与环境研究院）；Institut Agro（法国农学院）；PEGASE（畜牧业、环境与系统生理学研究部）
A Dataset of Dengue Hospitalizations in Brazil (1999 to 2021) with Weekly Disaggregation from Monthly Counts
Dept. of Control and Automation Engineering, UNESP（控制与自动化工程系，UNESP）；Dept. of Environmental Science, UNESP（环境科学系，UNESP）；HairU Serviços e Tecnologia LTDA（HairU服务与技术有限公司）；recod.ai, UNICAMP（recod.ai，UNICAMP）
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）
Deep sr-DDL: Deep Structurally Regularized Dynamic Dictionary Learning to Integrate Multimodal and Dynamic Functional Connectomics data for Multidimensional Clinical Characterizations
Johns Hopkins University（约翰斯·霍普金斯大学）；Kennedy Krieger Institute（肯尼迪·克里格研究所）；Johns Hopkins School of Medicine（约翰斯·霍普金斯医学院）
Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI with Limited Data
Charité - Universitätsmedizin Berlin（柏林夏里特医学院）；Berlin Institute of Health（柏林健康研究所）；Physikalisch-Technische Bundesanstalt (PTB)（德国联邦物理技术研究院（PTB））；King’s College London（伦敦国王学院）
