4,368 research outputs found
Unsupervised training methods for non-intrusive appliance load monitoring from smart meter data
No full textNon-intrusive appliance load monitoring (NIALM) is the process of disaggregating a household’s total electricity consumption into its contributing appliances. Smart meters are currently being deployed on national scales, providing a platform to collect aggregate household electricity consumption data. Existing approaches to NIALM require a manual training phase in which either sub-metered appliance data is collected or appliance usage is manually labelled. This training data is used to build models of the house- hold appliances, which are subsequently used to disaggregate the household’s electricity data. Due to the requirement of such a training phase, existing approaches do not scale automatically to the national scales of smart meter data currently being collected.In this thesis we propose an unsupervised training method which, unlike existing approaches, does not require a manual training phase. Instead, our approach combines general appliance knowledge with just aggregate smart meter data from the household to perform disaggregation. To do so, we address the following three problems: (i) how to generalise the behaviour of multiple appliances of the same type, (ii) how to tune general knowledge of appliances to the specific appliances within a single household using only smart meter data, and (iii) how to provide actionable energy saving advice based on the tuned appliance knowledge.First, we propose an approach to the appliance generalisation problem, which uses the Tracebase data set to build probabilistic models of household appliances. We take a Bayesian approach to modelling appliances using hidden Markov models, and empirically evaluate the extent to which they generalise to previously unseen appliances through cross validation. We show that learning using multiple appliances vastly outperforms learning from a single appliance by 61–99% when attempting to generalise to a previously unseen appliance, and furthermore that such general models can be learned from only 2–6 appliances.Second, we propose an unsupervised solution to the model tuning problem, which uses only smart meter data to learn the behaviour of the specific appliances in a given house-hold. Our approach uses general appliance models to extract appliance signatures from ?a household’s smart meter data, which are then used to refine the general appliance models. We evaluate the benefit of this process using the Reference Energy Disaggregation Data set, and show that the tuned appliance models more accurately represent the energy consumption behaviour of a given household’s appliances compared to when general appliance models are used, and furthermore that such general models can per- form comparably to when sub-metered data is used for model training. We also show that our tuning approach outperforms the current state of the art, which uses a factorial hidden Markov model to tune the general appliance models.Third, we apply both of these approaches to infer the energy efficiency of refrigerators and freezers in a data set of 117 households. We evaluate the accuracy of our approach, and show that it is able to successfully infer the energy efficiency of combined fridge freezers. We then propose an extension to our model tuning process using factorial hidden semi-Markov models to model households with a separate fridge and freezer. Finally, we show that through this extension our approach is able to simultaneously tune the appliance models of both appliances.The above contributions provide a solution which satisfies the requirements of a NIALM training method which is both unsupervised (no manual interaction required during training) and uses only smart meter data (no installation of additional hardware is required). When combined, the contributions presented in this thesis represent an advancement in the state of the art in the field of non-intrusive appliance load monitoring, and a step towards increasing the efficiency of energy consumption within households
The British ‘Bluesman’ Paul Oliver and the Nature of Transatlantic Blues Scholarship
No full textRecent revisionist studies have argued that much of what is known about music known as the blues’ has been 'invented' by the writing of enthusiasts far removed from the African American culture that created the music. Elijah Wald and Marybeth Hamilton in particular have attempted to sift through the clouds of romanticism, and tried to unveil more empirical histories that were previously obscured by the fallacious genre distinctions conjured up during the 1960s blues revival. While this revisionist scholarship has shed light on some previously ignored historical facts, writers have tended to concentrate on the romanticism of blues writing strictly from an American perspective, failing to acknowledge the genesis and influence of transatlantic scholarship, and therefore ignoring the work of the most prolific and influential blues scholar of the twentieth century, British writer Paul Oliver. By examining the core of Oliver’s research and writing during the 1950s and 1960s, this study aims to place Oliver in his rightful place at the centre of blues historiography. His scholarship allows a more detailed appreciation of the manner in which the blues was studied, through lyrics, recordings, oral histories, photography and African American literature. These historical sources were interpreted in accordance with the author’s attitudes to the commercial popular music, which allowed the ‘reconstruction’ of an African American ‘folk’ culture in which the blues became the antithesis of pop. Importantly, this study seeks to transcend dominant discourses of national cultural ownership or ethnocentrism, and demonstrate that representations of African American music and culture were constructed within a transatlantic context. The blues is music with roots in the African American experience within the United States; however, as Paul Oliver’s writing shows, its reception and representation were not limited by the same national, cultural or racial boundaries
Detecting anomalies in activities of daily living of elderly residents via energy disaggregation and Cox processes
No full textMonitoring the health of the elderly living independently in their own homes is a key issue in building sustainable healthcare models which support a country’s ageing population. Existing approaches have typically proposed remotely monitoring the behaviour of a household’s occupants through the use of additional sensors. However the costs and privacy concerns of such sensors have significantly limited their potential for widespread adoption. In contrast, in this paper we propose an approach which detects Activities of Daily Living, which we use as a proxy for the health of the household residents. Our approach detects appliance usage from existing smart meter data, from which the unique daily routines of the household occupants are learned automatically via a log Gaussian Cox process. We evaluate our approach using two real-world data sets, and show it is able to detect over 80% of kettle uses while generating less than 10% false positives. Furthermore, our approach allows earlier interventions in households with a consistent routine and fewer false alarms in the remaining households, relative to a fixed-time intervention benchmark
The Country Parson in the Eighteenth-Century Novel
No full textThe occasion for this study was an awareness of a need for an isolated examination of the country parson figure in the English novel. Owing to the limits of time and space regulating this research, its scope has been limited to the seminal period of the novel in the eighteenth century, and even further limited to three novels whose publication spans the century. These novels are Joseph Andrews, The Vicar of Wakefield, and Pride and Prejudice. An attempt has been made to investigate the parsons in the other writings of these novelists. The country parson has always been a central character in English society; successors to the "types" of country parson in the eighteenth-century novels can be found in Victorian novels such as Samuel Butler's The Way of all Flesh, and twentieth-century fiction. This study examines the nature of the relationship between the parson as portrayed in the eighteenth-century novel, the parson as portrayed in previous literature and the parson as a real figure in eighteenth-century society. The broad scope of this work has necessitated the use of a wide variety of historical, ecclesiastical and literary sources. It has been necessary to inquire into church history and social history of the eighteenth century, and also into the different theological factions of that age. The backgrounds of the country parson in English literature has been briefly summarized by looking at works of major importance. An attempt has been made to examine certain novels and novelists contemporary with and also succeeding the writers this work principally studies. Finally, this study utilizes the research of theorists who have written about the novel form, or about the relationship between history and literature. The movement of this study proceeds chronologically from the beginning to the end of the eighteenth century, tracing the evolution of the country parson in the novel and society. Joseph Andrews was published in 1743 and represents the concern with Latitudinarian Christianity of that time and Fielding's specific devotion to social and church reform. The Vicar of Wakefield (1766) represents the sentimental interest in rural retirement of mid-century, and Mr. Collins in Pride and Prejudice (1813) is the secular country parson, who entered the church at the end of the century, when the value of church livings had risen. This study attempts to show that as a character, the country parson has unique qualities which are shared by no other "type" of character, and which render him especially valuable; as it were, a lighthouse from which can be more clearly viewed the very fabric of individual and social life rendered in the novel.Master of Arts (MA
A hidden Markov model-based acoustic cicada detector for crowdsourced smartphone biodiversity monitoring
No full textIn recent years, the field of computational sustainability has striven to apply artificial intelligence techniques to solve ecological and environmental problems. In ecology, a key issue for the safeguarding of our planet is the monitoring of biodiversity. Automated acoustic recognition of species aims to provide a cost-effective method for biodiversity monitoring. This is particularly appealing for detecting endangered animals with a distinctive call, such as the New Forest cicada. To this end, we pursue a crowdsourcing approach, whereby the millions of visitors to the New Forest, where this insect was historically found, will help to monitor its presence by means of a smartphone app that can detect its mating call. Existing research in the field of acoustic insect detection has typically focused upon the classification of recordings collected from fixed field microphones. Such approaches segment a lengthy audio recording into individual segments of insect activity, which are independently classified using cepstral coefficients extracted from the recording as features. This paper reports on a contrasting approach, whereby we use crowdsourcing to collect recordings via a smartphone app, and present an immediate feedback to the users as to whether an insect has been found. Our classification approach does not remove silent parts of the recording via segmentation, but instead uses the temporal patterns throughout each recording to classify the insects present. We show that our approach can successfully discriminate between the call of the New Forest cicada and similar insects found in the New Forest, and is robust to common types of environment noise. A large scale trial deployment of our smartphone app collected over 6000 reports of insect activity from over 1000 users. Despite the cicada not having been rediscovered in the New Forest, the effectiveness of this approach was confirmed for both the detection algorithm, which successfully identified the same cicada through the app in countries where the same species is still present, and of the crowdsourcing methodology, which collected a vast number of recordings and involved thousands of contributors.</p
Non-intrusive load monitoring using prior models of general appliance types
No full textNon-intrusive appliance load monitoring is the process of disaggregating a household's total electricity consumption into its contributing appliances. In this paper we propose an approach by which individual appliances can be iteratively separated from an aggregate load. Unlike existing approaches, our approach does not require training data to be collected by sub-metering individual appliances, nor does it assume complete knowledge of the appliances present in the household. Instead, we propose an approach in which prior models of general appliance types are tuned to specific appliance instances using only signatures extracted from the aggregate load. The tuned appliance models are then used to estimate each appliance's load, which is subsequently subtracted from the aggregate load. This process is applied iteratively until all appliances for which prior behaviour models are known have been disaggregated. We evaluate the accuracy of our approach using the REDD data set, and show the disaggregation performance when using our training approach is comparable to when sub-metered training data is used. We also present a deployment of our system as a live application and demonstrate the potential for personalised energy saving feedback
Using hidden Markov models for iterative non-intrusive appliance monitoring
No full textNon-intrusive appliance load monitoring is the process of breaking down a household’s total electricity consumption into its contributing appliances. In this paper we propose an approach by which individual appliances are iteratively separated from the aggregate load. Our approach does not require training data to be collected by sub-metering individual appliances. Instead, prior models of general appliance types are tuned to specific appliance instances using only signatures extracted from the aggregate load. The tuned appliance models are used to estimate each appliance’s load, which is subsequently subtracted from the aggregate load. We evaluate our approach using the REDD data set, and show that it can disaggregate 35% of a typical household’s total energy consumption to an accuracy of 83% by only disaggregating three of its highest energy consuming appliances
NILMTK: An open source toolkit for non-intrusive load monitoring
No full textNon-intrusive load monitoring, or energy disaggregation, aims to separate household energy consumption data collected from a single point of measurement into appliance-level consumption data. In recent years, the field has rapidly expanded due to increased interest as national deployments of smart meters have begun in many countries. However, empirically comparing disaggregation algorithms is currently virtually impossible. This is due to the different data sets used, the lack of reference implementations of these algorithms and the variety of accuracy metrics employed. To address this challenge, we present the Non-intrusive Load Monitoring Toolkit (NILMTK); an open source toolkit designed specifically to enable the comparison of energy disaggregation algorithms in a reproducible manner. This work is the first research to compare multiple disaggregation approaches across multiple publicly available data sets. Our toolkit includes parsers for a range of existing data sets, a collection of preprocessing algorithms, a set of statistics for describing data sets, two reference benchmark disaggregation algorithms and a suite of accuracy metrics. We demonstrate the range of reproducible analyses which are made possible by our toolkit, including the analysis of six publicly available data sets and the evaluation of both benchmark disaggregation algorithms across such data sets
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