Sequence-to-point learning with neural networks for nonintrusive load monitoring

Energy disaggregation (a.k.a nonintrusive load monitoring, NILM), a single-channel blind source separation problem, aims to decompose the mains which records the whole house electricity consumption into appliance-wise readings. This problem is difficult because it is inherently unidentifiable. Recent approaches have shown that the identifiability problem could be reduced by introducing domain knowledge into the model. Deep neural networks have been shown to be a promising approach for these problems, but sliding windows are necessary to handle the long sequences which arise in signal processing problems, which raises issues about how to combine predictions from different sliding windows. In this paper, we propose sequence-to-point learning, where the input is a window of the mains and the output is a single point of the target appliance. We use convolutional neural networks to train the model. Interestingly, we systematically show that the convolutional neural networks can inherently learn the signatures of the target appliances, which are automatically added into the model to reduce the identifiability problem. We applied the proposed neural network approaches to real-world household energy data, and show that the methods achieve state-of-the-art performance, improving two standard error measures by 84% and 92%

Power estimation of multiple two-state loads using a probabilistic non-intrusive approach

This paper investigates a non-intrusive approach of retrieving electric space heater (ESH) power profiles from a residential aggregated signal. In cold-climate regions with heating appliances controlled by electronic thermostats, an accurate non-intrusive recognition of power profiles is a challenging task. Accordingly, a robust disaggregation approach based on the difference factorial hidden Markov model (DFHMM) and the Kronecker operation is contributed. The proposed method aims to uncover the underlying stochastic tow-state models of ESHs using their common prior knowledge. The major advantage of the developed load-monitoring architecture consists of modeling simplicity and inference as well as load-detection efficacy in the presence of perturbations from other unknown loads. The experimental results prove the effectiveness of the method in manipulating the challenging case of multiple two-state loads with a high event overlapping probability

Hardware and software platforms to deploy and evaluate non-intrusive load monitoring systems

The work in this PhD thesis addresses the practical implications of deploying and testing Non-Intrusive Load Monitoring (NILM) and eco-feedback solutions in real-world scenarios. The contributions to this topic are centered around the design and development of NILM frameworks that have been deployed in the wild, supporting long-term research in ecofeedback and also serving the purpose of producing real-world datasets and furthering the state of the art regarding the performance metrics used to evaluate NILM algorithms. This thesis consists of three main parts: i) the development of tools and datasets for NILM and eco-feedback research, ii) the design, implementation and deployment of NILM and eco-feedback technologies in real world scenarios, and iii) an experimental comparison of performance metrics for event detection and event classification algorithms. In the first part we describe the Energy Monitoring and Disaggregation Data Format (EMD-DF) and the SustData and SustDataED public datasets. In second part we discuss the development and deployment of two hardware and software platforms in real households, to support eco-feedback research. We then report on more than five years of experience in deploying and maintaining such platforms. Our findings suggest that the main practical issues can be divided in two categories, technological (e.g., system installation) and social (e.g., maintaining a steady sample throughout the whole study). In the final part of this thesis we analyze experimentally the behavior of a number of performance metrics for event detection and event classification, identifying clusters and relationships between the different measures. Our results evidence some considerable differences in the behavior of the performance metrics when applied to the different problems.O trabalho desenvolvido nesta tese de doutoramento aborda as implicações praticas da instalação e avaliação de soluções de monitorização não intrusiva de cargas elétricas (NILM) e eco-feedback em cenários reais. As contribuições para este tópico estão centradas em torno da concepção e desenvolvimento de plataformas NILM que foram instaladas em ambientes não controlados, suportando a pesquisa de longo termo em eco-feedback e servindo também o propósito de produzir conjuntos de dados científicos, bem como promover o avanço do estado da arte acerca das métricas de desempenho utilizadas para avaliar algoritmos NILM. Esta tese é constituída por três partes principais: i) o desenvolvimento de ferramentas e conjuntos de dados científicos para investigação em NILM e eco-feedback, ii) a concepção, desenho e instalação de tecnologias NILM e eco-feedback em cenários reais, e iii) uma comparação experimental de métricas de desempenho para algoritmos de detecção e de classificação de eventos. Na primeira parte descrevemos o Energy Monitoring and Disaggregation Data Format (EMD-DF) e os conjuntos de dados científicos SustData e SustDataED. Na segunda parte discutimos o desenvolvimento e instalação de duas plataformas de hardware e software em residências atuais com a finalidade de suportar a investigação em eco-feedback. Aqui, reportamos sobre mais de cinco anos de experiência na instalação e manutenção destes sistemas. Os nossos resultados sugerem que as principais implicações práticas podem ser divididas em duas categorias, físicas (e.g., instalação do sistema) e sociais (e.g., manter uma amostra constante ao longo de todo o estudo). Na terceira parte analisamos experimentalmente o comportamento de uma série de métricas de desempenho quando estas são utilizadas para avaliar algoritmos de detecção e de classificação de eventos. Calculamos as correlações lineares e não lineares entre os vários pares de métricas, e com base nesses valores procuramos agrupar as métricas que evidenciam um comportamento semelhante. Os nossos resultados sugerem a existência de diferenças evidentes no comportamento das métricas quando aplicadas a ambos dos problemas.Fundação para a Ciência e a Tecnologi