1,720,957 research outputs found
Analysing the potential of open hotel review databases for IEQ assessment: a text mining approach
No full textIndoor Environmental Quality (IEQ) significantly affects occupants’ well-being and comfort. Assessing IEQ typically involves post-occupancy evaluation (POE), a method that can be time-consuming and particularly challenging in hotel settings, where guests may be disrupted by frequent requests for feedback. Hence, this paper investigates the capability of text mining to extract valuable information for IEQ assessment, such as identifying the main causes of IEQ dissatisfaction, detecting combined occurrences of IEQ aspects, and exploring the relationship between IEQ dissatisfaction and hotel attractiveness. To this aim, the study analysed 1494 five-star hotels in Europe, comprising 515,738 reviews. Among them, 13.1% contained references to keywords related to IEQ aspects. The major cause of dissatisfaction in hotels is acoustic (42.7% of the reviews), followed by thermal (35.7%), visual (11.1%) comfort, and IAQ (10.5%). Additionally, 9580 reviews demonstrated the co-occurrence of multiple IEQ aspects, highlighting the interplay between different aspects. Furthermore, the reviewer score, reflecting the hotel’s attractiveness, showed an inverse relationship with the percentage of dissatisfied guests regarding IEQ, highlighting the impact of the indoor environment on the hotel rating. Overall, text mining is effective in supporting IEQ assessment and the study underscores the effect of addressing IEQ aspects on a facility's overall appeal
Towards the integration of energy performance certificates (EPC) and simplified building performance simulations using machine learning: initial findings
No full textEnergy performance certificates (EPCs) are useful tools that not only provide an indication on the efficiency of buildings, but also help raising awareness on the importance of improving their performance. However, the current implementation of EPCs is affected by issues that prevent their full potential to be exploited: lack of standardization across countries, frequency of errors and complexity of the calculation method are some examples. This study assesses the possibility of using machine learning as an alternative to the current quasi-steady state calculation method and represents a first step towards the development of a hybrid calculation tool
Building energy models with morphological urban-scale parameters: A case study in Turin
No full textWith a growing awareness around the importance of the optimization of building efficiency, being able to make accurate predictions of building energy demand is an invaluable asset for practitioners and designers. For this reason, it is important to continually improve existing models as well as introduce new methods that can help reduce the so-called energy performance gap, which separates predicted from actual consumption values. This is particularly true for urban scale simulations, where even small scenes can be very complex and carry the necessity of finding a reasonable balance between precision and computational efforts. The scope of this work is to present two different models that make use of morphological urban-scale parameters to improve their performances, taking into account the interactions between buildings and their surroundings. In order to do this, two neighbourhoods in the city of Turin (IT) were taken as case studies. The buildings studied present similar characteristics but are inserted in a different urban context. Several urban parameters were extracted using a GIS tool and used as input, alongside the building-scale features, for two different models: i) a bottom-up engineering approach that evaluates the energy balance of residential buildings and introduces some variables at block-of-buildings scale, ii) a machine learning approach based on the bootstrap aggregating (bagging) algorithm, which takes the same parameters used by the previous model as inputs and makes an estimation of the hourly energy consumption of each building. The main results obtained confirm that the urban context strongly influences the energy performance of buildings located in high built-up areas, and that introducing simple morphological urban-scale parameters in the models to take these effects into account can improve their performance while having a very low impact on the computational efforts
Development and comparison of adaptive data-driven models for thermal comfort assessment and control
Full text linkThermal comfort prediction is an important issue, as it can largely influence occupants’ well-being and buildings’ energy consumption. Nowadays, models used to assess thermal comfort have been increasingly discussed, and a growing number of data-driven models with several input parameters developed. Although these models allow reasonably accurate predictions of thermal comfort, using complex algorithms to determine thermal comfort might be unsuitable for some use cases, such as quick estimations or real-time control of Heating, Ventilation, and Air Conditioning (HVAC) systems. In this paper, a data-driven model was developed based on 61710 samples of subjective responses associated with environmental parameters from field studies available in two ASHRAE databases. Two models resulted from this analysis, one with higher accuracy and one simplified, which improved the prediction in comparison to other regression models and PMV. However, since thermal comfort cannot be conceived as a punctual condition, comfort areas were derived, i.e., respective comfort ranges at 90%, 80%, and 70% of thermal acceptability. The result is that the error in the prediction of the new models is below the 90% acceptable range, which means that the models' error does not lead to a reduction in the evaluation of occupant comfort. Built upon influential parameters, these models enable thermal comfort estimates and occupant-centered HVAC control. The notion of comfort as a non-fixed state empowers more flexible building management criteria, reducing energy use while upholding indoor comfort
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
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