Robust energy disaggregation using appliance-specific temporal contextual information

An extension of the baseline non-intrusive load monitoring approach for energy disaggregation using temporal contextual information is presented in this paper. In detail, the proposed approach uses a two-stage disaggregation methodology with appliance-specific temporal contextual information in order to capture time-varying power consumption patterns in low-frequency datasets. The proposed methodology was evaluated using datasets of different sampling frequency, number and type of appliances. When employing appliance-specific temporal contextual information, an improvement of 1.5% up to 7.3% was observed. With the two-stage disaggregation architecture and using appliance-specific temporal contextual information, the overall energy disaggregation accuracy was further improved across all evaluated datasets with the maximum observed improvement, in terms of absolute increase of accuracy, being equal to 6.8%, thus resulting in a maximum total energy disaggregation accuracy improvement equal to 10.0%.Peer reviewedFinal Published versio

NILM techniques for intelligent home energy management and ambient assisted living: a review

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora: Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve 01/SAICT/2018/39578 Fundação para a Ciência e Tecnologia through IDMEC, under LAETA: SFRH/BSAB/142998/2018 SFRH/BSAB/142997/2018 UID/EMS/50022/2019 Junta de Comunidades de Castilla-La-Mancha, Spain: SBPLY/17/180501/000392 Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project): TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio

Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation

In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.Peer reviewedFinal Published versio

Energy Disaggregation using Two-Stage Fusion of Binary Device Detectors

A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method is using a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets (ECO, REDD and iAWE), which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and non-linear appliances across all evaluated datasets

GroEL ring separation and exchange in the chaperonin reaction

The bacterial chaperonin GroEL and its cofactor GroES form a nano-cage for a single molecule of substrate protein (SP) to fold in isolation. GroEL and GroES undergo an ATP-regulated interaction cycle that governs the closing and opening of the folding cage. GroEL consists of two heptameric rings, stacked back-to-back, and displays intra-ring positive allosteric cooperativity and inter-ring negative allostery. Previous reports have suggested that ring separation and exchange can occur between the non-covalently bound rings of GroEL; however, the mechanism and physiological function of this phenomenon had yet to be explained. Here I show that GroEL undergoes transient ring separation, resulting in ring exchange between complexes. Through the ATPase cycling of GroEL/ES, ring separation is shown to occur upon ATP-binding to the trans-ring of the asymmetric GroEL:7ADP:GroES complex in the presence or absence of SP. Ring separation is a consequence of inter-ring negative allostery. To address the physiological function of this phenomenon, I created a novel mutant with the two rings connected by disulfide bonds. This GroEL mutant, unable to perform ring separation, is folding-active but populates symmetric GroEL:GroES2 complexes with GroES bound to both rings of GroEL, where both GroEL rings function simultaneously rather than sequentially. As a consequence, SP binding and release from the folding chamber is inefficient, and E. coli growth is impaired. My results suggest that transient ring separation is an integral part of the chaperonin mechanism to ensure sequential GroEL/ES cycling and effective SP folding

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

Physical properties of red blood cells in aggregation

Red blood cells (RBC) are micron-sized biological objects and the main corpuscular constituent of blood. It flows from larger arteries to very small capillaries. Utilizing a physical approach, this work aims to assess properties that govern blood flows and in particular the disaggregation and aggregation mechanisms of RBC at a single cell level. The interactions of RBCs are thus, investigated experimentally by measuring adhesive forces in the pN range in various model solutions thanks to optical tweezers. While two models for aggregation have been proposed: bridging and depletion, experimental evidence is still lacking to decide which mechanism prevails. The research presented here provides a new insight on the aggregation of RBCs and shows that the two models may not be exclusive. A complete 3-dimensional phase diagram of doublets has been established and confirmed by experiments by varying the adhesive forces and reduced cell volumes. Besides, the effect of aggregation was studied in vitro in a bifurcating microcapillary network and the distribution of aggregates and their stability in such a geometry are reported. Finally, experiments in flow allowed the characterization of the flow field around single RBCs at different velocities. Interesting vortical fluid structures have been also observed thanks to tracer nanoparticles.Rote Blutkörperchen (Erythrozyten) sind biologische Objekte im Mikrometerbereich und der korpuskuläre Hauptbestandteil des Blutes. Es fließt aus größeren Arterien in sehr kleine Kapillaren. Unter Verwendung eines physikalischen Ansatzes zielt diese Arbeit darauf ab, die Eigenschaften zu bewerten, die den Blutfluss und insbesondere die Disaggregations- und Aggregationsmechanismen der RBC auf Einzelzellebene regeln. Die Interaktionen der Erythrozyten werden daher experimentell untersucht, indem Adhäsionskräfte im pN-Bereich in verschiedenen Modelllösungen mit Hilfe einer optischen Pinzette gemessen werden. Während mit Bridging und Depletion zwei Modelle für die Aggregation vorgeschlagen wurden, fehlen noch experimentelle Beweise, um zu entscheiden, welcher Mechanismus vorherrscht. Die hier vorgestellte Forschung liefert neue Erkenntnisse über die Aggregation von RBCs und zeigt, dass die beiden Modelle möglicherweise nicht exklusiv sind. Es wurde ein vollständiges dreidimensionales Phasendiagramm von Dubletten erstellt und experimentell durch Variation der Adhäsionskräfte und reduzierte Zellvolumina bestätigt. Außerdem wurde der Effekt der Aggregation in vitro in einem sich gabelförmigen Mikrokapillarnetz untersucht, und es wird über die Verteilung der Aggregate und ihre Stabilität in einer solchen Geometrie berichtet. Schließlich erlaubten Strömungsexperimente die Charakterisierung des Strömungsfeldes um einzelne RBCs bei unterschiedlichen Geschwindigkeiten. Dank Tracer-Nanopartikeln konnten auch interessante wirbelartige Fluidstrukturen beobachtet werden