A Dynamical Systems Approach to Energy Disaggregation

Energy disaggregation, also known as non-intrusive load monitoring (NILM), is the task of separating aggregate energy data for a whole building into the energy data for individual appliances. Studies have shown that simply providing disaggregated data to the consumer improves energy consumption behavior. However, placing individual sensors on every device in a home is not presently a practical solution. Disaggregation provides a feasible method for providing energy usage behavior data to the consumer which utilizes currently existing infrastructure. In this paper, we present a novel framework to perform the energy disaggregation task. We model each individual device as a single-input, single-output system, where the output is the power consumed by the device and the input is the device usage. In this framework, the task of disaggregation translates into finding inputs for each device that generates our observed power consumption. We describe an implementation of this framework, and show its results on simulated data as well as data from a small-scale experiment.Comment: Submitted to 52nd IEEE Conference on Decision and Control (CDC 2013

Fundamental Limits of Nonintrusive Load Monitoring

Provided an arbitrary nonintrusive load monitoring (NILM) algorithm, we seek bounds on the probability of distinguishing between scenarios, given an aggregate power consumption signal. We introduce a framework for studying a general NILM algorithm, and analyze the theory in the general case. Then, we specialize to the case where the error is Gaussian. In both cases, we are able to derive upper bounds on the probability of distinguishing scenarios. Finally, we apply the results to real data to derive bounds on the probability of distinguishing between scenarios as a function of the measurement noise, the sampling rate, and the device usage.Comment: Submitted to the 3rd ACM International Conference on High Confidence Networked Systems (HiCoNS

Energy Disaggregation via Adaptive Filtering

The energy disaggregation problem is recovering device level power consumption signals from the aggregate power consumption signal for a building. We show in this paper how the disaggregation problem can be reformulated as an adaptive filtering problem. This gives both a novel disaggregation algorithm and a better theoretical understanding for disaggregation. In particular, we show how the disaggregation problem can be solved online using a filter bank and discuss its optimality.Comment: Submitted to 51st Annual Allerton Conference on Communication, Control, and Computin

Load Demand Disaggregation Based on Simple Load Signature and User's Feedback

Abstract A detailed and on-line knowledge of the electrical load demand by the users is a critical issue for an effective and responsive deployment of home/building energy management. An approach based on the application of Non Intrusive Appliance Load Monitoring (NIALM) techniques copes with the goal of disaggregating composite loads; but to get a high level of precision, NIALM algorithms need a complete load signature and complex optimization algorithms to find the right combination of single loads that fits the real electrical measurements. On the other hand, it is practically impossible to get the detailed signature of all appliances inside a house/building and sophisticated optimization algorithm are not suitable for on-line applications. To overcome such problems a straightforward NIALM algorithm is proposed, it is based on both a simple load signature, rated active and reactive power and a heuristic disaggregation algorithm. Of course, it is expected that on real applications, this approach cannot reach very high performances; this is the reason why an active involvement of users is considered. The users' feedback aims to: correct the load signatures, reduce the error of disaggregation algorithm and increase the active participation of users in saving energy politics. The NIALM algorithm has been accurately tested numerically using as input load curves generated randomly but under given constraints. In this way, the causes of inefficiency of the proposed approach are quantitatively analyzed both separately and in different combinations. Finally, the increase of the efficiency of the NIALM algorithm due to the application of different feedback actions is evaluated and discussed

Assinaturas baseadas no espaço de escalas de curvatura aplicadas ao monitoramento não invasivo de cargas elétricas residenciais

Non-intrusive load monitoring (NILM) systems have gained extensive interest due to their potential role regarding power savings for residential customers. These systems, which are mostly based on stages of detection and classification of transients on aggregated signals, rely heavily on load signatures. In the literature, the image-based representations of voltagecurrent (V-I) trajectories are claimed as the most effective individual steady-state signatures for appliance classification. However, these representations inherit some drawbacks from their generation process and they are thus incapable of inheriting all the information encompassed by V-I trajectories. This work then proposes two steady-state appliance signatures derived from the curvature scale space of V-I trajectories. These signatures aim to improve the image representations of V-I trajectories by encompassing structural elements related to the general shape of such trajectories as well as some characteristics neglected during their generation. A group of load signatures formed from the proposed signatures was evaluated on direct load classification and load disaggregation scenarios for four publicly available datasets. The results achieved by the proposed representations surpassed the sole employment of a reference image-based V-I signature for all the test scenarios executed. Also, some of the evaluated signatures outperformed all known proposals that are exclusively based on steady-state signatures for load classification on a given benchmark dataset as well as on two other public datasets.Agência 1Os sistemas de monitoramento não invasivo de cargas elétricas (MNICE) têm recebido extensivo interesse em função de seu potencial em prover informações que podem resultar em economia no consumo de energia elétrica residencial. Esses sistemas são baseados na análise de sinais agregados de consumo de energia elétrica e, em sua grande parte, também em etapas de detecção e de classificação de transientes em tais sinais, o que os torna fortemente dependentes de assinaturas de cargas elétricas residenciais. Na literatura, as trajetórias tensão-corrente (V-I) são assumidas como as representações mais completas para cargas elétricas residenciais, de tal modo que suas representações em imagem são supostas como as assinaturas de estado estacionário mais efetivas para cargas elétricas residenciais. No entanto, essas assinaturas herdam limitações de seus processos de obtenção que as tornam incapazes de incorporar toda a informação contida nas trajetórias que representam. Este trabalho de tese então propõe duas novas assinaturas de estado estacionário para cargas elétricas residenciais, as quais são pretendidas como melhorias para as assinaturas em imagem citadas. As assinaturas propostas são derivadas do espaço de escalas de curvatura de trajetórias tensão-corrente e assim são capazes de realçar a representação de elementos estruturais quanto à forma geral de tais trajetórias. Elas também são capazes de incorporar certas características negligenciadas pelas representações em imagem de tais trajetórias. Um conjunto de assinaturas derivado das assinaturas propostas foi avaliado em cenários com dados submedidos e também com dados de consumo agregado provenientes de quatro bases de dados públicas. Os resultados obtidos pelas assinaturas avaliadas superaram o desempenho obtido pelo emprego isolado de uma representação em imagem da trajetória V-I adotada como referência. Ademais, alguns dos resultados obtidos também suplantaram trabalhos de estado da arte em três bases de dados, dentre elas uma base de dados que é tida como de referência para testes de classificação de cargas elétricas residenciais