1,209 research outputs found
Low-rate non-intrusive load monitoring approaches via graph signal processing
The large-scale roll-out of smart metering worldwide brings many new application possibilities. One promising application is appliance-level energy feedback based on identifying individual loads from aggregate measurements. Driven by high application potentials, the research in this area has intensified. In particular, non-intrusive load monitoring (NILM), that is, estimating appliance load consumption from aggregate readings, using software means only, has attracted a lot of attention, since it does not require any additional hardware to be installed.
This thesis first proposes two Graph Signal Processing (GSP)-based approaches for disaggregation of total energy consumption down to individual appliances used. The first approach uses the Graph Laplacian Regularisation (GLR) minimiser results as a starting point, adding further refinement via Simulated Annealing (SA). The second approach applies data segmentation and associates data segments with graph nodes. A Dynamic Time Warping (DTW) distance is applied for evaluating weights between graph nodes. GLR minimiser is again used for clustering.
Finally, a generic optimisation based approach is proposed for improving the accuracy of existing NILM by minimising the difference between the measured aggregate load and the sum of estimated individual loads with the difference from original NILM approaches' results as regularisation. For all proposed methods, the competitive performance are demonstrated in terms of both accuracy and effciency compared to state-of-the-art approaches, using the public Personalised Retrofit Decision Support Tools For UK Homes Using Smart Home Technology (REFIT) dataset and Reference Energy Disaggregation Dataset (REDD) electrical load datasets.The large-scale roll-out of smart metering worldwide brings many new application possibilities. One promising application is appliance-level energy feedback based on identifying individual loads from aggregate measurements. Driven by high application potentials, the research in this area has intensified. In particular, non-intrusive load monitoring (NILM), that is, estimating appliance load consumption from aggregate readings, using software means only, has attracted a lot of attention, since it does not require any additional hardware to be installed.
This thesis first proposes two Graph Signal Processing (GSP)-based approaches for disaggregation of total energy consumption down to individual appliances used. The first approach uses the Graph Laplacian Regularisation (GLR) minimiser results as a starting point, adding further refinement via Simulated Annealing (SA). The second approach applies data segmentation and associates data segments with graph nodes. A Dynamic Time Warping (DTW) distance is applied for evaluating weights between graph nodes. GLR minimiser is again used for clustering.
Finally, a generic optimisation based approach is proposed for improving the accuracy of existing NILM by minimising the difference between the measured aggregate load and the sum of estimated individual loads with the difference from original NILM approaches' results as regularisation. For all proposed methods, the competitive performance are demonstrated in terms of both accuracy and effciency compared to state-of-the-art approaches, using the public Personalised Retrofit Decision Support Tools For UK Homes Using Smart Home Technology (REFIT) dataset and Reference Energy Disaggregation Dataset (REDD) electrical load datasets
Digital ecosystems
We view Digital Ecosystems to be the digital counterparts of biological ecosystems, which
are considered to be robust, self-organising and scalable architectures that can automatically
solve complex, dynamic problems. So, this work is concerned with the creation, investigation,
and optimisation of Digital Ecosystems, exploiting the self-organising properties of biological
ecosystems. First, we created the Digital Ecosystem, a novel optimisation technique inspired
by biological ecosystems, where the optimisation works at two levels: a first optimisation,
migration of agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on evolutionary computing
that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant
constraints. We then investigated its self-organising aspects, starting with an extension
to the definition of Physical Complexity to include the evolving agent populations of our
Digital Ecosystem. Next, we established stability of evolving agent populations over time,
by extending the Chli-DeWilde definition of agent stability to include evolutionary dynamics.
Further, we evaluated the diversity of the software agents within evolving agent populations,
relative to the environment provided by the user base. To conclude, we considered alternative
augmentations to optimise and accelerate our Digital Ecosystem, by studying the accelerating
effect of a clustering catalyst on the evolutionary dynamics of our Digital Ecosystem, through
the direct acceleration of the evolutionary processes. We also studied the optimising effect of
targeted migration on the ecological dynamics of our Digital Ecosystem, through the indirect
and emergent optimisation of the agent migration patterns. Overall, we have advanced the
understanding of creating Digital Ecosystems, the self-organisation that occurs within them,
and the optimisation of their Ecosystem-Oriented Architecture
Parallel Graph Partitioning for Complex Networks
Processing large complex networks like social networks or web graphs has
recently attracted considerable interest. In order to do this in parallel, we
need to partition them into pieces of about equal size. Unfortunately, previous
parallel graph partitioners originally developed for more regular mesh-like
networks do not work well for these networks. This paper addresses this problem
by parallelizing and adapting the label propagation technique originally
developed for graph clustering. By introducing size constraints, label
propagation becomes applicable for both the coarsening and the refinement phase
of multilevel graph partitioning. We obtain very high quality by applying a
highly parallel evolutionary algorithm to the coarsened graph. The resulting
system is both more scalable and achieves higher quality than state-of-the-art
systems like ParMetis or PT-Scotch. For large complex networks the performance
differences are very big. For example, our algorithm can partition a web graph
with 3.3 billion edges in less than sixteen seconds using 512 cores of a high
performance cluster while producing a high quality partition -- none of the
competing systems can handle this graph on our system.Comment: Review article. Parallelization of our previous approach
arXiv:1402.328
An Analysis of the Genetic Algorithm and Abstract Search Space Visualisation
The Genetic Algorithm (Holland, 1975) is a powerful search technique based upon the
principles of Darwinian evolution. In its simplest form the GA consists of three main
operators - crossover, mutation and selection. The principal theoretical treatment of
the Genetic Algorithm (GA) is provided by the Schema Theorem and building block
hypothesis (Holland, 1975). The building block hypothesis describes the GA search
process as the combination, sampling and recombination of fragments of solutions
known as building blocks. The crossover operator is responsible for the combination
of building blocks, whilst the selection operator allocates increasing numbers of
samples to good building blocks. Thus the GA constructs the optimal (or near-optimal)
solution from those fragments of solutions which are, in some sense, optimal.
The first part of this thesis documents the development of a technique for the isolation
of building blocks from the populations of the GA. This technique is shown to extract
exactly those building blocks of interest - those which are sampled most regularly by
the GA. These building blocks are used to empirically investigate the validity of the
building block hypothesis. It is shown that good building blocks do not combine to
form significantly better solution fragments than those resulting from the addition of
randomly generated building blocks to good building blocks. This results casts some
doubt onto the value of the building block hypothesis as an account of the GA search
process (at least for the functions used during these experiments).
The second part of this thesis describes an alternative account of the action of
crossover. This account is an approximation of the geometric effect of crossover upon
the population of samples maintained by the GA. It is shown that, for a simple
function, this description of the crossover operator is sufficiently accurate to warrant
further investigation. A pair of performance models for the GA upon this function are
derived and shown to be accurate for a wide range of crossover schemes. Finally, the
GA search process is described in terms of this account of the crossover operator and
parallels are drawn with the search process of the simulated annealing algorithm
(Kirkpatrick et al, 1983).
The third and final part of this thesis describes a technique for the visualisation of high
dimensional surfaces, such as are defined by functions of many parameters. This
technique is compared to the statistical technique of projection pursuit regression
(Friedman & Tukey, 1974) and is shown to compare favourably both in terms of
computational expense and quantitative accuracy upon a wide range of test functions.
A fundamental flaw of this technique is that it may produce poor visualisations when
applied to functions with a small high frequency (or order) components
Non-intrusive load monitoring solutions for low- and very low-rate granularity
Strathclyde theses - ask staff. Thesis no. : T15573Large-scale smart energy metering deployment worldwide and the integration of smart meters within the smart grid are enabling two-way communication between the consumer and energy network, thus ensuring an improved response to demand. Energy disaggregation or non-intrusive load monitoring (NILM), namely disaggregation of the total metered electricity consumption down to individual appliances using purely algorithmic tools, is gaining popularity as an added-value that makes the most of meter data.In this thesis, the first contribution tackles low-rate NILM problem by proposing an approach based on graph signal processing (GSP) that does not require any training.Note that Low-rate NILM refers to NILM of active power measurements only, at rates from 1 second to 1 minute. Adaptive thresholding, signal clustering and pattern matching are implemented via GSP concepts and applied to the NILM problem. Then for further demonstration of GSP potential, GSP concepts are applied at both, physical signal level via graph-based filtering and data level, via effective semi-supervised GSP-based feature matching. The proposed GSP-based NILM-improving methods are generic and can be used to improve the results of various event-based NILM approaches. NILM solutions for very low data rates (15-60 min) cannot leverage on low to highrates NILM approaches. Therefore, the third contribution of this thesis comprises three very low-rate load disaggregation solutions, based on supervised (i) K-nearest neighbours relying on features such as statistical measures of the energy signal, time usage profile of appliances and reactive power consumption (if available); unsupervised(ii) optimisation performing minimisation of error between aggregate and the sum of estimated individual loads, where energy consumed by always-on load is heuristically estimated prior to further disaggregation and appliance models are built only by manufacturer information; and (iii) GSP as a variant of aforementioned GSP-based solution proposed for low-rate load disaggregation, with an additional graph of time-of-day information.Large-scale smart energy metering deployment worldwide and the integration of smart meters within the smart grid are enabling two-way communication between the consumer and energy network, thus ensuring an improved response to demand. Energy disaggregation or non-intrusive load monitoring (NILM), namely disaggregation of the total metered electricity consumption down to individual appliances using purely algorithmic tools, is gaining popularity as an added-value that makes the most of meter data.In this thesis, the first contribution tackles low-rate NILM problem by proposing an approach based on graph signal processing (GSP) that does not require any training.Note that Low-rate NILM refers to NILM of active power measurements only, at rates from 1 second to 1 minute. Adaptive thresholding, signal clustering and pattern matching are implemented via GSP concepts and applied to the NILM problem. Then for further demonstration of GSP potential, GSP concepts are applied at both, physical signal level via graph-based filtering and data level, via effective semi-supervised GSP-based feature matching. The proposed GSP-based NILM-improving methods are generic and can be used to improve the results of various event-based NILM approaches. NILM solutions for very low data rates (15-60 min) cannot leverage on low to highrates NILM approaches. Therefore, the third contribution of this thesis comprises three very low-rate load disaggregation solutions, based on supervised (i) K-nearest neighbours relying on features such as statistical measures of the energy signal, time usage profile of appliances and reactive power consumption (if available); unsupervised(ii) optimisation performing minimisation of error between aggregate and the sum of estimated individual loads, where energy consumed by always-on load is heuristically estimated prior to further disaggregation and appliance models are built only by manufacturer information; and (iii) GSP as a variant of aforementioned GSP-based solution proposed for low-rate load disaggregation, with an additional graph of time-of-day information
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