1,720,965 research outputs found
Explainable spatio-temporal Graph Neural Networks for multi-site photovoltaic energy production
Full text linkIn recent years, there has been a growing demand for renewable energy sources, which are inherently associated with a decentralized distribution and dependent on weather conditions. Their management and associated forecasting of produced energy are tasks of increasing complexity. Spatio-Temporal Graph Neural Networks have been applied in this context with excellent results, taking advantage of the correct integration of both topological data, defined by the distribution of the plants in the territory, and temporal data of the time series. A drawback of graph neural networks is the recurrent mechanism adopted to process the temporal part, which increases greatly the computational load of these models. Moreover, these models are formulated for real and sensitive contexts where, in addition to being accurate, the predictions must also be understandable by the human operator. For these reasons, in this paper we propose a novel explainable energy forecasting framework based on Spatio-Temporal Graph Neural Networks: the forecasting model generates predictions by processing temporal and spatial information using a spectral graph convolution and a 1D convolutional neural network respectively, then we apply a state-of-the-art explainer to them in order to produce explanations about the generation process. Our proposed method obtains predictions having better performance than previous approaches, both in terms of computational efficiency and prediction accuracy, with the possibility of interpreting them in order to understand the generation process. The novel approach based on fusion of forecasting and explainability in a single framework enables the creation of powerful and reliable systems suitable for real-world issues and challenges
Multi-site Forecasting of Energy Time Series with Spatio-Temporal Graph Neural Networks
No full textClimate change has prompted the energy sector to shift its focus to renewable energy sources, which are environmentally friendly but less in terms of cost, complexity, and plants' management. It becomes critical to have a reliable method for estimating the output power of these systems, which are dispersed across the country and vary in kind and technology, and whose output power is mostly determined by meteorological factors. In this paper, we exploit the capability of modeling dynamic graph-like data of a specific type of graph neural network, spatio-temporal graph neural network, which can process spatial information about plants' distribution in a particular region as well as temporal data on individual plant power production. Plants in the same region can share information and make more accurate forecasts in this way. The suggested model was evaluated on two types of datasets: one with data gathered from real photovoltaic systems and the other with synthesized power time series reconstructed from data acquired by satellite detection. Our studies discovered how these systems can estimate the production outputs of photovoltaic stations simultaneously and with higher accuracy with respect to previous state-of-the-art models, performing effectively even in the absence of meteorological data
On the exploration of graph state-space models for spatio-temporal renewable energy forecasting
No full textIn the face of increasing demand for accurate energy forecasting, State-Space Models have arisen as an effective method for spatiotemporal prediction in renewable energy systems. This preliminary study explores the application of graph-based state-space models to renewable energy datasets, aiming at enhancing the accuracy and reliability of energy forecasts. We investigate the performance of these models in capturing the complex spatiotemporal dependencies inherent in energy production data from diverse renewable sources, represented by solar and wind power plants. We employ the Spatial-Temporal Graph Mamba model as a benchmark for validating the state-space model mechanism in the forecasting problem. Our experiments indicate that state-space models offer promising capabilities in forecasting energy output with improved precision over traditional methods and reduced computational cost. We also discuss the implications of these findings for future research and the potential for integrating state-space models into real-world energy management systems
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
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