27 research outputs found
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
Residential Energy Management for Renewable Energy Systems Incorporating Data-Driven Unravelling of User Behavior
The penetration of distributed energy resources (DERs) such as photovoltaic (PV) at the residential
level has increased rapidly over the past year. It will inevitably induce a paradigm shift in end-user
and operations of local energy markets. The energy community with high integration of DERs
initiative allows its users to manage their generation (for prosumers) and consumption more
efficiently, resulting in various economic, social, and environmental benefits. Specifically, the local
energy communities and their members can legally engage in energy generation, distribution, supply,
consumption, storage, and sharing to increase levels of autonomy from the power grid, advance
energy efficiency, reduce energy costs, and decrease carbon emissions. Reducing energy
consumption costs is difficult for residential energy management without understanding the users'
preferences. The advanced measurement and communication technologies provide opportunities for
individual consumers/prosumers and local energy communities to adopt a more active role in
renewable-rich smart grids. Non-intrusive load monitoring (NILM) monitors the load activities from a
single point source, such as a smart meter, based on the assumption that different appliances have
different power consumption levels and features. NILM can extract the users' load consumption from
the smart meter to support the development of the smart grid for better energy management and
demand response (DR). Yet to date, how to design residential energy management, including home
energy management systems (HEMS) and community energy management systems (CEMS), with
an understanding of user preferences and willingness to participate in energy management, is still far
from being fully investigated. This thesis aims to develop methodologies for a resident energy
management system for renewable energy systems (RES) incorporating data-driven unravelling of
the user's energy consumption behaviour
Neural Natural Language Processing for Long Texts: A Survey of the State-of-the-Art
The adoption of Deep Neural Networks (DNNs) has greatly benefited Natural
Language Processing (NLP) during the past decade. However, the demands of long
document analysis are quite different from those of shorter texts, while the
ever increasing size of documents uploaded on-line renders automated
understanding of long texts a critical area of research. This article has two
goals: a) it overviews the relevant neural building blocks, thus serving as a
short tutorial, and b) it surveys the state-of-the-art in long document NLP,
mainly focusing on two central tasks: document classification and document
summarization. Sentiment analysis for long texts is also covered, since it is
typically treated as a particular case of document classification.
Additionally, this article discusses the main challenges, issues and current
solutions related to long document NLP. Finally, the relevant, publicly
available, annotated datasets are presented, in order to facilitate further
research.Comment: 53 pages, 2 figures, 171 citation
Feature Reduction and Representation Learning for Visual Applications
Computation on large-scale data spaces has been involved in many active problems in computer vision and pattern recognition. However, in realistic applications, most existing algorithms are heavily restricted by the large number of features, and tend to be inefficient and even infeasible. In this thesis, the solution to this problem is addressed in the following ways: (1) projecting features onto a lower-dimensional subspace; (2) embedding features into a Hamming space.
Firstly, a novel subspace learning algorithm called Local Feature Discriminant Projection (LFDP) is proposed for discriminant analysis of local features. LFDP is able to efficiently seek a subspace to improve the discriminability of local features for classification. Extensive experimental validation on three benchmark datasets demonstrates that the proposed LFDP outperforms other dimensionality reduction methods and achieves state-of-the-art performance for image classification. Secondly, for action recognition, a novel binary local representation for RGB-D video data fusion is presented. In this approach, a general local descriptor called Local Flux Feature (LFF) is obtained for both RGB and depth data by computing the local fluxes of the gradient fields of video data. Then the LFFs from RGB and depth channels are fused into a Hamming space via the Structure Preserving Projection (SPP), which preserves not only the pairwise feature structure, but also a higher level connection between samples and classes. Comprehensive experimental results show the superiority of both LFF and SPP. Thirdly, in respect of unsupervised learning, SPP is extended to the Binary Set Embedding (BSE) for cross-modal retrieval. BSE outputs meaningful hash codes for local features from the image domain and word vectors from text domain. Extensive evaluation on two widely-used image-text datasets demonstrates the superior performance of BSE compared with state-of-the-art cross-modal hashing methods. Finally, a generalized multiview spectral embedding algorithm called Kernelized Multiview Projection (KMP) is proposed to fuse the multimedia data from multiple sources. Different features/views in the reproducing kernel Hilbert spaces are linearly fused together and then projected onto a low-dimensional subspace by KMP, whose performance is thoroughly evaluated on both image and video datasets compared with other multiview embedding methods
Energy Data Analytics for Smart Meter Data
The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal
Recent Developments in Smart Healthcare
Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine