1,720,959 research outputs found
Enhanced spatio-temporal electric load forecasts using less data with active deep learning
No full textAryandoust, A., Patt, A. & Pfenninger, S. Enhanced spatio-temporal electric load forecasts using less data with active deep learning. Nature Machine Intelligence 4, 977–991 (2022). https://doi.org/10.1038/s42256-022-00552-
City-scale car traffic and parking density maps from Uber Movement travel time data
No full textAryandoust, A., van Vliet, O. & Patt, A. City-scale car traffic and parking density maps from Uber Movement travel time data. Scientific Data 6, 158 (2019). https://doi.org/10.1038/s41597-019-0159-
Modelling and Optimization Based Control for Demand Response in Active Distribution Networks
Get PDFWe explore how Demand Response (DR) can effectively provide electricity
system services such as for the management of bi-directional power flows and
the control of voltage deviations in active distribution networks, without
compromising consumer comfort or adversely affecting grid operation in the
transmission network. By translating intricate power system physics into
straightforward control objectives, we design DR control algorithms that can
operate within realistic computational time frames at scale. We conduct
simulation-based experiments and find that minimizing the Euclidean-Norm of the
total residual load at transformer sub-stations is an effective objective for
harnessing DR for the dispatch of renewable electricity grids. We show that
this control objective can be efficiently pursued in sequential order, without
optimal power flow calculations or information about the topology of a grid.
Additionally, we find that pursuing this objective reduces the sum of peak
power flows along all lines in active distribution networks, and can therefore
enable both lower transmission losses and lower voltage deviations
Enhanced spatio-temporal electric load forecasts using less data with active deep learning
Get PDFAn effective way to oppose global warming and mitigate climate change is to
electrify our energy sectors and supply their electric power from renewable
wind and solar. Spatio-temporal predictions of electric load become
increasingly important for planning this transition, while deep learning
prediction models provide increasingly accurate predictions for it. The data
used for training deep learning models, however, is usually collected at random
using a passive learning approach. This naturally results in a large demand for
data and associated costs for sensors like smart meters, posing a large barrier
for electric utilities in decarbonizing their grids. Here, we test active
learning where we leverage additional computation for collecting a more
informative subset of data. We show how electric utilities can apply active
learning to better distribute smart meters and collect their data for more
accurate predictions of load with about half the data compared to when applying
passive learning
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Get PDFWe 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
- …
