1,721,050 research outputs found
Increasing reliability of bottom-up building-stock energy models using available data-driven techniques
No full textWith the most recent, unprecedented energy crisis ongoing, in which the strain on energy supply and reserves has skyrocketed the energy pricing and the demand for renewable energy systems, popular attention has been drawn once more to energy use in buildings. Since the first energy crisis in the early 70s and later the awareness of climate change, the building energy sector has been revolving around energy conservation and energy efficiency such that they are no longer just buzzwords. Nevertheless, the road towards net zero energy in buildings is far from completed and further strains are needed.
To do so, simulation models have become increasingly prominent tools to aid decision-making processes and building energy policy making as they allow for the quick evaluation of competing policy options concerning the best energy conservation recommendations in the building sector.
However, a large number of large-scale statistical studies in different countries on the gap between the real and regulatory calculated building energy use, from the last decade, revealed that the regulatory calculation methods (i.e. simplified (white-box) Building-Stock Energy Models) largely overestimated the real energy use of existing, old residential buildings (thus dwellings where energy conservation measures are most needed), inflated true energy savings and undermined national energy policy making.
The regulatory calculation methods thus prove to be inaccurate predictors of the real energy use in residential buildings. The prediction errors (i.e. the gap between the real and the theoretical energy use in buildings) vary largely from one house and household to the other and above all, the predictions are not accurate on average either. Also the predicted energy savings are rarely achieved.
This PhD-dissertation contributes to research on the gap between the real and theoretical energy use in buildings, focusing on methods to increase the reliability of bottom-up Building-Stock Energy Models using available data-driven techniques. Using data from more than 250,000 Flemish single-family houses, this research builds further on existing studies on the gap between simplified (white-box) regulatory calculation methods and real energy use in buildings by contributing results for Flanders. It further tries to identify suitable data-driven models (white-box/black-box/grey-box) that allow for reliable, accurate and fast predictions of the energy use and energy savings in buildings that can be used at building stock level, aid decision-making processes and allow for building energy policy making. Additionally, this research studies a number of model evaluation techniques for Building-Stock Energy Models during model development and model validation that must assure reliable and robust model results and inferences and thus model quality.
The first part of this PhD-dissertation situates the research in the broader context of energy use in buildings, the existing regulatory performance assessment methods and the gap between the real and regulatory calculated building energy use (Chapter 1). Further, relevant background literature about the use of large-scale building datasets in stock models, the types of building stock models for predicting building energy use and other performance indicators is presented and the reader is introduced to the types and the treatment of uncertainty in BSEMs (Chapter 2).
In Chapter 3, a number of model evaluation techniques for Building-Stock Energy Models are studied that must assure reliable and robust model results and inferences and thus model quality. The chapter proposes a methodology (that is scalable) to apply Uncertainty (UA) and Sensitivity Analysis (SA) to BSEMs with an emphasis on important methodological aspects: input parameter sampling procedure, minimum required building stock size and number of samples needed for convergence and proves the importance of executing a UA-SA in well-thought-out fashion. Also (i) the performance of common UA-SA methods was studied in order to (ii) recommend appropriate (thus reliable, robust and computationally efficient) methods for the the aimed UA-SA target: parameter screening, ranking or indices (based on the evaluations in (i)).
Using data from more than 250,000 Flemish single-family houses, Chapter 4 builds further on existing studies on the gap between simplified (white-box) regulatory calculation methods and real energy use in buildings by contributing results for Flanders. The chapter also describes the stock datasets that are then used in the following three modelling chapters, Chapter 5, 6 and 7, as input and validation data. Results from statistical analysis showed that the overestimation of the real energy use in buildings for space heating and domestic hot water (and thus also the total energy use) was exceedingly large for existing single-family houses compared to studies from other EU countries. The Flemish EPC labels proved to be very poor indicators of the real energy use in residential buildings.
Chapter 5 examines to what extent available aggregated variables explain the real annual energy use in buildings using classical statistical linear regression models and addresses the problem of multicollinearity and the importance of bootstrapped confidence intervals for model quality control. Further, based on the regression coefficients, inferences are drawn about possible causes for the gap between real and regulatory energy use. The results showed that statistical linear models explained only a fraction of all variability and indicated that a significant extent of multicollinearity had to be corrected. For most models, half of the variability has been left unexplained and has to be attributed to variables that were not available, the fact that the data were insufficiently accurate or that the model (structures) were not good enough.
Similarly to Chapter 5, Chapter 6 examines to what extent available aggregated variables explain the real annual energy use in buildings, yet using common black-box machine learning regression techniques such as gradient boosting regression trees and support vector regression. Similar to the results for the linear regression models, the results for the machine learning models showed that only a fraction of all variability could be explained. Half of the variability has been left unexplained and has to be attributed to variables that were not available, inaccurate data or the fact that the model (structures) were not good enough.
Last, Chapter 7 presents a white-box model identification procedure that allows to develop simple dynamic white-box models for the modelling of the individual buildings within bottom-up BSEMs that predict common BSEM outputs (i.e. yearly heat demand, peak load and heat load curve). Dynamic simple white-box RC models are established through pattern searches between the parameter estimates from simple stochastic grey-box models and aggregated white-box measurement data. The identified models showed great performance for predicting the heat demand in residential buildings at stock level. Not only were they able to accurately predict the average and the variability in the yearly net energy use for space heating within the boundaries of the modelled building stock, they also showed accurate predictions of the peak load and a median heat load curve ánd performed relatively well outside the boundaries of the modelled building stock and for different weather data.
Combined, the conclusions from the different chapters, summed up in the eight and last chapter, demonstrate that the the developed (grey-box trained) simple white-box models are assumed to work well within dynamic building stock energy modelling frameworks that allow to study future policy plans. As such, the author believes that the future of building stock energy studies and pathways towards net zero lie in bottom-up BSEMs with grey-box trained simple white-box models under the hood
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
Author Under Sail The Imagination of Jack London, 1893-1902
No full textIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Intro -- Title Page -- Copyright Page -- Dedication -- Contents -- Acknowledgments -- Introduction -- 1. Spirit Truth -- 2. From Absorption to Theatricality and Back Again -- 3. "I Will Build a New Present" -- 4. Sons as Authors -- 5. Fathers as Publishers -- 6. The Daughter as Author -- 7. Lovers as Authors -- 8. At Sea with the Family -- 9. Yellow News, Yellow Stories -- 10. The Return Home -- Notes -- Bibliography -- Index -- About Jay WilliamsIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, YYYY. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
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