Machine learning models have proven to be reliable methods in classification tasks. However, little research has been done on classifying dwelling characteristics based on smart meter & weather data before. Gaining insights into dwelling characteristics can be helpful to create/improve the policies for creating new dwellings at NZEB standard. This paper compares the different machine learning algorithms and the methods used to correctly implement the models. These methods include the data pre-processing, model validation and evaluation. Smart meter data was provided by Groene Mient, which was used to train several machine learning algorithms. The models that were generated by the algorithms were compared on their performance. The results showed that Recurrent Neural Network (RNN) 2performed the best with 96% of accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrices were used to produce classification reports, which can indicate which of the models work the best for this specific problem. The models were programmed in Python.
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Machine learning models have proven to be reliable methods in classification tasks. However, little research has been conducted on the classification of dwelling characteristics based on smart meter and weather data before. Gaining insights into dwelling characteristics, which comprise of the type of heating system used, the number of inhabitants, and the number of solar panels installed, can be helpful in creating or improving the policies to create new dwellings at nearly zero-energy standard. This paper compares different supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Long-short term memory, and methods used to correctly implement these algorithms. These methods include data pre-processing, model validation, and evaluation. Smart meter data, which was used to train several machine learning algorithms, was provided by Groene Mient. The models that were generated by the algorithms were compared on their performance. The results showed that the Long-short term memory performed the best with 96% accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrics were used to produce classification reports, which indicates that the Long-short term memory outperforms the compared models on the evaluation metrics for this specific problem.
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Deze bijdrage aan "Smart Humanity" (red. W. Bronsgeest en S. de Waart 2020) schetst een beeld van modellen die richting geven aan processen, organisatorische inrichting, en informatievoorziening. AI-toepassingen worden hierbij gezien als onderdeel van de informatievoorziening.
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Smart glasses were perceived to be potentially revolutionary for healthcare, however, there is only limited research on the acceptance and social implications of smart glasses in healthcare. This study aims to get a better insight into the theoretical foundations and the purpose was to identify themes regarding adoption, mediation, and the use of smart glasses from the perspective of healthcare professionals. A qualitative research design with focus groups was used to collect data. Three focus groups with 22 participants were conducted. Data were analyzed using content analysis. Our analysis revealed six overarching themes related to the anticipated adoption of smart glasses: knowledge, innovativeness, use cases, ethical issues, persuasion, and attitude. Nine themes were found related to anticipated mediation and use of smart glasses: attention, emotions, social influences, design, context, camera use, risks, comparisons to known products, and expected reaction and might influence the acceptance of smart glasses.
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Smart glasses were perceived to be potentially revolutionary for healthcare, however, there is only limited research on the acceptance and social implications of smart glasses in healthcare. This study aims to get a better insight into the theoretical foundations and the purpose was to identify themes regarding adoption, mediation, and the use of smart glasses from the perspective of healthcare professionals. A qualitative research design with focus groups was used to collect data. Three focus groups with 22 participants were conducted. Data were analyzed using content analysis. Our analysis revealed six overarching themes related to the anticipated adoption of smart glasses: knowledge, innovativeness, use cases, ethical issues, persuasion, and attitude. Nine themes were found related to anticipated mediation and use of smart glasses: attention, emotions, social influences, design, context, camera use, risks, comparisons to known products, and expected reaction and might influence the acceptance of smart glasses.
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Contribution to conference magazine https://husite.nl/ssc2017/ Conference ‘Smart Sustainable Cities 2017 – Viable Solutions’ The conference ‘Smart Sustainable Cities 2017 – Viable Solutions’ was held on 14 June 2017 in Utrecht, the Netherlands. Over 250 participants from all over Europe attended the conference.
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Author supplied: Within the Netherlands the interest for sustainability is slowly growing. However, most organizations are still lagging behind in implementing sustainability as part of their strategy and in developing performance indicators to track their progress; not only in profit organizations but in higher education as well, even though sustainability has been on the agenda of the higher educational sector since the 1992 Earth Summit in Rio, progress is slow. Currently most initiatives in higher education in the Netherlands have been made in the greening of IT (e.g. more energy efficient hardware) and in implementing sustainability as a competence in curricula. However if we look at the operations (the day to day processes and activities) of Dutch institutions for higher education we just see minor advances. In order to determine what the best practices are in implementing sustainable processes, We have done research in the Netherlands and based on the results we have developed a framework for the smart campus of tomorrow. The research approach consisted of a literature study, interviews with experts on sustainability (both in higher education and in other sectors), and in an expert workshop. Based on our research we propose the concept of a Smart Green Campus that integrates new models of learning, smart sharing of resources and the use of buildings and transport (in relation to different forms of education and energy efficiency). Flipping‐the‐classroom, blended learning, e‐learning and web lectures are part of the new models of learning that should enable a more time and place independent form of education. With regard to smart sharing of resources we have found best practices on sharing IT‐storage capacity among universities, making educational resources freely available, sharing of information on classroom availability and possibilities of traveling together. A Smart Green Campus is (or at least is trying to be) energy neutral and therefore has an energy building management system that continuously monitors the energy performance of buildings on the campus. And the design of the interior of the buildings is better suited to the new forms of education and learning described above. The integrated concept of Smart Green Campus enables less travel to and from the campus. This is important as in the Netherlands about 60% of the CO2 footprint of a higher educational institute is related to mobility. Furthermore we advise that the campus is in itself an object for study by students and researchers and sustainability should be made an integral part of the attitude of all stakeholders related to the Smart Green Campus. The Smart Green Campus concept provides a blueprint that Dutch institutions in higher education can use in developing their own sustainability strategy. Best practices are shared and can be implemented across different institutions thereby realizing not only a more sustainable environment but also changing the attitude that students (the professionals of tomorrow) and staff have towards sustainability.
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Abstract The Government of the Netherlands wants to be energy neutral by 2050 (Rijksoverheid, sd). A transition towards non-fossil energy sources also affects transport, which is one of the industries significantly contributing to CO2 emission (Centraal Bureau Statistiek, 2019). Road authorities at municipalities and provinces want a shift from fossil fuel-consuming to zero-emission transport choices by their inhabitants. For this the Province of Utrecht has data available. However, they struggle how to deploy data to positively influence inhabitants' mobility behavior. A problem analysis scoped the research and a survey revealed the gap between the province's current data-item approach that is infrastructure oriented and the required approach that adopts traveler’s personas to successfully stimulate cycling. For this more precisely defined captured data is needed and the focus should shift from already motivated cyclists to non-cyclers.
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From the list of content: " Smart sustainable cities & higher education, Essence: what, why & how? Developing learning materials together; The blended learning environment; Teaching on entrepreneurship; Utrecht municipality as a client; International results; Studentexperiences; International relations; City projects in Turku, Alcoy and Utrecht ".
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B4B is a multi-year, multi-stakeholder project focused on developing methods to harness big data from smart meters, building management systems and the Internet of Things devices, to reduce energy consumption, increase comfort, respond flexibly to user behaviour and local energy supply and demand, and save on installation maintenance costs. This will be done through the development of faster and more efficient Machine Learning and Artificial Intelligence models and algorithms. The project is geared to existing utility buildings such as commercial and institutional buildings.
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