26 research outputs found
Anonymization of Sensitive Quasi-Identifiers for l-diversity and t-closeness
A number of studies on privacy-preserving data mining have been proposed. Most of them assume that they can separate quasi-identifiers (QIDs) from sensitive attributes. For instance, they assume that address, job, and age are QIDs but are not sensitive attributes and that a disease name is a sensitive attribute but is not a QID. However, all of these attributes can have features that are both sensitive attributes and QIDs in practice. In this paper, we refer to these attributes as sensitive QIDs and we propose novel privacy models, namely, (l1, ..., lq)-diversity and (t1, ..., tq)-closeness, and a method that can treat sensitive QIDs. Our method is composed of two algorithms: an anonymization algorithm and a reconstruction algorithm. The anonymization algorithm, which is conducted by data holders, is simple but effective, whereas the reconstruction algorithm, which is conducted by data analyzers, can be conducted according to each data analyzer’s objective. Our proposed method was experimentally evaluated using real data sets
Identification Method for Type-Ⅲ Industrial and Commercial Load Considering Identification Result Continuity
Non-intrusive load monitoring technology can guide users to arrange power consumption time reasonably, thereby reducing power consumption. Among them, due to the continuous variability of the state, the identification of continuously varying (Type-Ⅲ) load has always been one of the difficult problems in non-intrusive load monitoring. Aiming at the problem of Type-Ⅲ load identification, a non-intrusive load identification algorithm based on deep convolutional neural network (CNN) and hidden Markov model (HMM) is proposed. Firstly, the load characteristics are selected according to the mutual information theory. Then, the residual neural network is used as the basic structure of deep CNN to extract multi-dimensional features of the load and realize the initial identification of Type-Ⅲ loads. Finally, in order to solve the problem of state breakpoint in CNN identification results, the HMM is used to complete the continuous optimization of load identification results. In the complex industrial and commercial operation environment, the algorithm is trained and verified on the representative Type-Ⅲ load data. The results show that the proposed algorithm can effectively identify the operation state of Type-Ⅲ industrial and commercial load
Time-frequency analysis techniques for non-intrusive load monitoring
The work
in this thesis examines time-frequency analysis techniques and in particular the wavelet
transform to extract the features contained within the electrical load signals. A novel approach
that is based on wavelet design was utilized to generate a wavelet library which was used to
match each load signal to a specific wavelet using Procrustes and covariance analysis. In order
to automate the load identification process, two machine learning classifiers representing an
eager learner and a lazy learner were used in this work. The proposed wavelet design concept
has been verified experimentally, and the results of implementing the proposed load detection
and classification approach shows significant improvement in the classification accuracy
compared to other existing detection approaches reaching an overall accuracy of 98%
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
Predicting the Future
Due to the increased capabilities of microprocessors and the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that machine learning and Big Data have an important presence in the field of Energy. This Special Issue entitled “Predicting the Future—Big Data and Machine Learning” is focused on applications of machine learning methodologies in the field of energy. Topics include but are not limited to the following: big data architectures of power supply systems, energy-saving and efficiency models, environmental effects of energy consumption, prediction of occupational health and safety outcomes in the energy industry, price forecast prediction of raw materials, and energy management of smart buildings