scholarly journals Online Machine Learning of Available Capacity for Vehicle-to-Grid Services during the Coronavirus Pandemic

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7176
Author(s):  
Rob Shipman ◽  
Rebecca Roberts ◽  
Julie Waldron ◽  
Chris Rimmer ◽  
Lucelia Rodrigues ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Use ◽  
Grid Services ◽  
Vehicle To Grid ◽  
Observed Behaviour ◽  
Balance Energy ◽  
Critical Components ◽  
The One ◽  
The University ◽  
The Impact

Vehicle-to-grid services make use of the aggregated capacity available from a fleet of vehicles to participate in energy markets, help integrate renewable energy in the grid and balance energy use. In this paper, the critical components of such a service are described in the context of a commercial service that is currently under development. Key among these components is the prediction of available capacity at a future time. In this paper, we extend a previous work that used a deep learning recurrent neural network for this task to include online machine learning, which enables the network to continually refine its predictions based on observed behaviour. The coronavirus pandemic that was declared in 2020 resulted in closures of the university and substantial changes to the behaviour of the university fleet. In this work, the impact of this change in vehicles usage was used to test the predictions of a network initially trained using vehicle trip data from 2019 with and without online machine learning. It is shown that prediction error is significantly reduced using online machine learning, and it is concluded that a similar capability will be of critical importance for a commercial service such as the one described in this paper.

scholarly journals Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland

2021 ◽  
Vol 13 (4) ◽  
pp. 1595
Author(s):  
Valeria Todeschi ◽  
Roberto Boghetti ◽  
Jérôme H. Kämpf ◽  
Guglielmina Mutani
Keyword(s):  
Machine Learning ◽  
Energy Use ◽  
Residential Buildings ◽  
Building Energy ◽  
Energy Performance ◽  
Space Heating ◽  
Engineering Model ◽  
Building Energy Use ◽  
The Impact ◽  
The City

Building energy-use models and tools can simulate and represent the distribution of energy consumption of buildings located in an urban area. The aim of these models is to simulate the energy performance of buildings at multiple temporal and spatial scales, taking into account both the building shape and the surrounding urban context. This paper investigates existing models by simulating the hourly space heating consumption of residential buildings in an urban environment. Existing bottom-up urban-energy models were applied to the city of Fribourg in order to evaluate the accuracy and flexibility of energy simulations. Two common energy-use models—a machine learning model and a GIS-based engineering model—were compared and evaluated against anonymized monitoring data. The study shows that the simulations were quite precise with an annual mean absolute percentage error of 12.8 and 19.3% for the machine learning and the GIS-based engineering model, respectively, on residential buildings built in different periods of construction. Moreover, a sensitivity analysis using the Morris method was carried out on the GIS-based engineering model in order to assess the impact of input variables on space heating consumption and to identify possible optimization opportunities of the existing model.

scholarly journals Analysis of the Impact of Different Variables on the Energy Demand in Office Buildings

2020 ◽  
Vol 12 (13) ◽  
pp. 5347
Author(s):  
José Luis Fuentes-Bargues ◽  
José-Luis Vivancos ◽  
Pablo Ferrer-Gisbert ◽  
Miguel Ángel Gimeno-Guillem
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Energy Use ◽  
Computer Software ◽  
Energy Performance ◽  
Point Of View ◽  
Regulatory Systems ◽  
Energy Behaviour ◽  
Balance Energy ◽  
The Impact

The design of near zero energy offices is a priority, which involves looking to achieve designs which minimise energy consumption and balance energy requirements with an increase in the installation and consumption of renewable energy. In light of this, some authors have used computer software to achieve simulations of the energy behaviour of buildings. Other studies based on regulatory systems which classify and label energy use also generally make their assessments through the use of software. In Spain, there is an authorised procedure for certifying the energy performance of buildings, and software (LIDER-CALENER unified tool) which is used to demonstrate compliance of the performance of buildings both from the point of view of energy demand and energy consumption. The aim of this study is to analyse the energy behaviour of an office building and the variability of the same using the software in terms of the following variables: climate zone, building orientation and certain surrounding wall types and encasements typical of this type of construction.

scholarly journals Learning capacity: predicting user decisions for vehicle-to-grid services

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Rob Shipman ◽  
Sophie Naylor ◽  
James Pinchin ◽  
Rebecca Gough ◽  
Mark Gillott
Keyword(s):  
Machine Learning ◽  
Electric Vehicles ◽  
Supply And Demand ◽  
Learning Model ◽  
Grid Services ◽  
Vehicle To Grid ◽  
Learning Capacity ◽  
Machine Learning Model ◽  
Storage Resource ◽  
Ev Charging

AbstractThe electric vehicles (EV) market is projected to continue its rapid growth, which will profoundly impact the demand on the electricity network requiring costly network reinforcements unless EV charging is properly managed. However, as well as importing electricity from the grid, EVs also have the potential to export electricity through vehicle-to-grid (V2G) technology, which can help balance supply and demand and stabilise the grid through participation in flexibility markets. Such a scenario requires a population of EVs to be pooled to provide a larger storage resource. Key to doing so effectively however is knowledge of the users, as they ultimately determine the availability of a vehicle. In this paper we introduce a machine learning model that aims to learn both a) the criteria influencing users when they decided whether to make their vehicle available and b) their reliability in following through on those decisions, with a view to more accurately predicting total available capacity from the pool of vehicles at a given time. Using a series of simplified simulations, we demonstrate that the learning model is able to adapt to both these factors, which allows the required capacity of a market event to be satisfied more reliably and using a smaller number of vehicles than would otherwise be the case. This in turn has the potential to support participation in larger and more numerous market events for the same user base and use of the technology for smaller groups of users such as individual communities.

“Formalism” in Polish Literary Scholarship

Slavic Review ◽  
1972 ◽  
Vol 31 (3) ◽  
pp. 574-582
Author(s):  
Zbigniew Folejewski
Keyword(s):  
Short Story ◽  
Russian Language ◽  
The Novel ◽  
Literary Scholarship ◽  
Russian Formalism ◽  
Literary Research ◽  
Roman Jakobson ◽  
The One ◽  
The University ◽  
The Impact

In discussing the contribution of the Polish “Formal” or “Integral” School to the development of literary research, one of the difficulties is whether to view it mainly as an echo of Russian Formalism or as a scholarly movement in its own right. There is no doubt that the often strikingly suggestive theoretical slogans and undeniable practical achievements of the Russian Formalists—such as Shklovsky's insights on the theory of the novel, V. I. Propp's Morphology of the Folktale, M. A. Petrovsky's Morphology of the Short Story, and the research of Boris Tomashevsky, Viktor Zhirmunsky, and Roman Jakobson in the field of poetry—all greatly attracted those Polish scholars who were looking for a coherent, strictly literary set of criteria, discouraged as they were by the inflation of biographism and psychologism in literary research. Yet the impact of Russian Formalism was limited in scope and in many respects rather indirect. On the one hand, the reaction against the one-sidedness of the psychological school came in Poland independently, and in some ways even earlier than in Russia. For this the Polish scholars did not need to go to Russia—they had both ancient (Aristotle) and more modern sources (German, Italian, French, and others). On the other hand, many of the Polish scholars did not even know the Russian language, though they knew some Western languages very well. (The scholar who was to become the foremost promoter of Formalism, Manfred Kridl, knew very little Russian when he came to teach at the University of Wilno. It was under the influence and with the help of a group of students that he became familiar with the writings of the Formalists.)

scholarly journals Impact of the COVID-19 Pandemic on the Energy Use at the University of Almeria (Spain)

2021 ◽  
Vol 13 (11) ◽  
pp. 5843
Author(s):  
Mehdi Chihib ◽  
Esther Salmerón-Manzano ◽  
Mimoun Chourak ◽  
Alberto-Jesus Perea-Moreno ◽  
Francisco Manzano-Agugliaro
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Energy Savings ◽  
Electricity Consumption ◽  
Energy Performance ◽  
Energy Sector ◽  
Wide Range ◽  
University Buildings ◽  
The University ◽  
The Impact

The COVID-19 pandemic has caused chaos in many sectors and industries. In the energy sector, the demand has fallen drastically during the first quarter of 2020. The University of Almeria campus also declined the energy consumption in 2020, and through this study, we aimed to measure the impact of closing the campus on the energy use of its different facilities. We built our analysis based upon the dataset collected during the year 2020 and previous years; the patterns evolution through time allowed us to better understand the energy performance of each facility during this exceptional year. We rearranged the university buildings into categories, and all the categories reduced their electricity consumption share in comparison with the previous year of 2019. Furthermore, the portfolio of categories presented a wide range of ratios that varied from 56% to 98%, the library category was found to be the most influenced, and the research category was found to be the least influenced. This opened questions like why some facilities were influenced more than others? What can we do to reduce the energy use even more when the facilities are closed? The university buildings presented diverse structures that revealed differences in energy performance, which explained why the impact of such an event (COVID-19 pandemic) is not necessarily relevant to have equivalent variations. Nevertheless, some management deficiencies were detected, and some energy savings measures were proposed to achieve a minimum waste of energy.

scholarly journals The impact of COVID-19 epidemiological restriction guidelines measures in a Croatian tertiary colorectal cancer center

2020 ◽  
Vol 48 (2-3) ◽  
pp. 43-46
Author(s):  
Iva Kirac ◽  
◽  
Zvonimir Misir ◽  
Vesna Vorih ◽  
Loris Ćurt ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Colorectal Surgery ◽  
University Hospital ◽  
Cancer Center ◽  
Solid Cancer ◽  
Everyday Activities ◽  
The One ◽  
The University ◽  
The Impact ◽  
The Given

Background: In the past six months, Croatia faced a short lockdown and a slow return to most hospitals’ everyday activities. During the lockdown, our center, as a part of the University Hospital Centre specialized for solid cancer, was enabled to maintain most of the routine practices with the one-month colonoscopy exception. Aim: To determine the oscillation in the number of endoscopies and colorectal surgery for 13 months (six months pre and post COVID-19 lockdown). Materials and methods: From August 1st, 2019, until August 31st 2020, the hospital analytics determine the number of colonoscopies, screening colonoscopies, and surgeries. Results: During the given period number of detected and operated colorectal cancers was stable, except for April, when we mostly did not perform colonoscopies. Conclusion: We maintained a pre-COVID-19 pace in colorectal cancer treatment, colonoscopies, and colorectal surgery after epidemiological guidelines for colonoscopies and colorectal surgery were applied, owing to the relatively stable overall epidemiological situation.

scholarly journals The Impact of Advertising and R&D on Bankruptcy Survival: A Double-Edged Sword

2020 ◽  
Vol 84 (5) ◽  
pp. 22-40
Author(s):  
Niket Jindal
Keyword(s):  
Machine Learning ◽  
Research And Development ◽  
Cash Flow ◽  
The Other ◽  
Cumulative Effects ◽  
Future Cash Flow ◽  
Low Level ◽  
Out Of Sample ◽  
The One ◽  
The Impact

Advertising and research and development (R&D) benefit firms by increasing sales and shareholder value. However, when a firm is in bankruptcy, the cumulative effects of its past advertising and R&D can be a double-edged sword. On the one hand, they increase the firm’s expected future cash flow, which increases the likelihood that the bankruptcy court will decide the firm can survive. On the other hand, they increase the liquidation value of the firm’s assets, which decreases the likelihood that the bankruptcy court will decide that the firm can survive. The author argues that the ability of advertising and R&D to either increase or decrease bankruptcy survival is contingent on the influence that the firm’s suppliers have, relative to other creditors, on the bankruptcy court’s decision. Advertising and R&D increase (decrease) bankruptcy survival when suppliers have a high (low) level of influence. Empirical analyses, conducted on 1,504 bankruptcies, show that advertising (R&D) increases bankruptcy survival when at least 35%−38% (18%−21%) of the bankrupt firm’s debt has been borrowed from suppliers, whereas it decreases bankruptcy survival below this point. Out-of-sample machine learning validation shows that the ability to predict whether a bankrupt customer will survive is substantially improved by considering the firm’s advertising and R&D.

scholarly journals Investigation and prediction of Energy consumption at St. Olavs Hospital

2021 ◽  
Vol 246 ◽  
pp. 04003
Author(s):  
Kristofersen, by Hans Smedsrud ◽  
Kai Xue ◽  
Zhirong Yang ◽  
Liv-Inger Stenstad ◽  
Tor Emil Giske ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Operating Room ◽  
Energy Use ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Heating And Cooling ◽  
Margin Of Error ◽  
The Impact ◽  
Operational Data

The objective of this study is to evaluate and predict the energy use in different buildings during COVID-19 pandemic period at St. Olavs Hospital in Trondheim. Based on machine learning, operational data from St. Olavs hospital combined with weather data will be used to predict energy use for the hospital. Analysis of the energy data showed that the case buildings at the hospital did not have any different energy use during the pandemic this year compared to the same period last year, except for the lab center. The energy consumption of electricity, heating and cooling is very similar both in 2019 and 2020 for all buildings, but in 2020 during the pandemic, the lab center had a reduction of 35% in electricity, compared to last year. An analysis of the energy needed for heating and cooling in the end of June to the end of November was also calculated for operating room 1 and was estimated to 256 kWh/m2 for operation room 1. The machine learning algorithms perform very well to predict the energy consumption of case buildings, Random Forest and AdaBoost proves as the best models, with less than 10% margin of error, some of the models have only 4% error. An analysis of the effect of humidification of ventilation air on energy consumption in operating room 1 was also carried out. The impact on energy consumption were high in winter and will at the coldest periods be able to double the energy consumption needed in the ventilation.

scholarly journals Machine Learning and Big Data in the Impact Literature. A Bibliometric Review with Scientific Mapping in Web of Science

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 495 ◽  
Author(s):  
Jesús López Belmonte ◽  
Adrián Segura-Robles ◽  
Antonio-José Moreno-Guerrero ◽  
María Elena Parra-González
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
English Language ◽  
Web Of Science ◽  
Large Data ◽  
Explanatory Models ◽  
Scientific Production ◽  
Scientific Publications ◽  
The One ◽  
The Impact

Combined use of machine learning and large data allows us to analyze data and find explanatory models that would not be possible with traditional techniques, which is basic within the principles of symmetry. The present study focuses on the analysis of the scientific production and performance of the Machine Learning and Big Data (MLBD) concepts. A bibliometric methodology of scientific mapping has been used, based on processes of estimation, quantification, analytical tracking, and evaluation of scientific research. A total of 4240 scientific publications from the Web of Science (WoS) have been analyzed. Our results show a constant and ascending evolution of the scientific production on MLBD, 2018 and 2019 being the most productive years. The productions are mainly in English language. The topics are variable in the different periods analyzed, where “machine-learning” is the one that shows the greatest bibliometric indicators, it is found in most of motor topics and is the one that offers the greatest line of continuity between the different periods. It can be concluded that research on MLBD is of interest and relevance to the scientific community, which focuses its studies on the branch of machine-learning.

scholarly journals The New Entrepreneurship in Higher Education: The Impact of Tuition Increases at an Ontario University

2002 ◽  
Vol 32 (3) ◽  
pp. 85-109
Author(s):  
Linda Quirke ◽  
Scott Davies
Keyword(s):  
Higher Education ◽  
University Education ◽  
Tuition Fees ◽  
Low Socioeconomic ◽  
Canadian Higher Education ◽  
Lower Socioeconomic ◽  
The One ◽  
The University ◽  
The Impact ◽  
Tuition Costs

Recent increases in university tuition fees are part of a new entrepre- neurial trend in higher education in which institutions are expected to generate more of their own revenue. We examine the effects of this trend on access to universities for students of lower socioeconomic origins, and identify a series of cross cutting pressures. On the one hand, tuition fees pose an obvious financial barrier for these students, whom researchers have shown to be relatively cost-sensitive and debt-averse. On the other hand, the demand for university education among youth from all backgrounds remains buoyant, and student cultures may be increasingly resigned to accepting large debts to finance their schooling. We then examine empirical evidence from two surveys from the University of Guelph, along with some supplementary sources. We find that the representation of students from low socioeconomic backgrounds fell substantially during a decade of rising tuition costs. In discussing this finding, we link the phenomena of higher and de-regulated tuition to the new entrepreneurship, and argue that it has the potential to increasingly stratify Canadian higher education.

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