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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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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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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A world where technology is ubiquitous and embedded in our daily lives is becoming increasingly likely. To prepare our students to live and work in such a future, we propose to turn Saxion’s Epy-Drost building into a living lab environment. This will entail setting up and drafting the proper infrastructure and agreements to collect people’s location and building data (e.g. temperature, humidity) in Epy-Drost, and making the data appropriately available to student and research projects within Saxion. With regards to this project’s effect on education, we envision the proposal of several derived student projects which will provide students the opportunity to work with huge amounts of data and state-of-the-art natural interaction interfaces. Through these projects, students will acquire skills and knowledge that are necessary in the current and future labor-market, as well as get experience in working with topics of great importance now and in the near future. This is not only aligned with the Creative Media and Game Technologies (CMGT) study program’s new vision and focus on interactive technology, but also with many other education programs within Saxion. In terms of research, the candidate Postdoc will study if and how the data, together with the building’s infrastructure, can be leveraged to promote healthy behavior through playful strategies. In other words, whether we can persuade people in the building to be more physically active and engage more in social interactions through data-based gamification and building actuation. This fits very well with the Ambient Intelligence (AmI) research group’s agenda in Augmented Interaction, and CMGT’s User Experience line. Overall, this project will help spark and solidify lasting collaboration links between AmI and CMGT, give body to AmI’s new Augmented Interaction line, and increase Saxion’s level of education through the dissemination of knowledge between researchers, teachers and students.
The main aim of KiNESIS is to create a Knowledge Alliance among academia, NGOs, communities, local authorities, businesses to develop a program of multidisciplinary activities in shrinking areas with the aim of promoting and fostering ideas, projects, workforce, productivity and attractiveness. The problems affecting peripheral territories in rural or mountain areas of the interior regions, compared to small, medium or large population centres and large European capitals, are related to complex but clear phenomena: the emigration of young generations, abandonment and loneliness of elderly people, the loss of jobs, the deterioration of buildings and land, the closing of schools and related services, the disappearance of traditions and customs, the contraction of local governments, which in absence of adequate solutions can only generate worse conditions, leading to the abandonment of areas rich in history, culture and traditions. It is important that these communities - spread all over Europe - are not abandoned since they are rich in cultural traditions, which need to be preserved with a view to new developments, intended as "intelligent" rebirth and recovery.The focus of KiNESIS is to converge the interest of different stakeholders by recalling various skills around abandoned villages to make them "smart" and "attractive".Keeping in mind the triangular objectives of cooperation and innovation of research, higher education and business of the Knowledge Alliance action, the project aims are: i) revitalising depopulated areas by stimulating entrepreneurship and entrepreneurial skills; ii) creating local living laboratories, shared at European level, in which the exchange of knowledge, best practices, experiences can help promote social inclusion and entrepreneurial development;iii) experimenting new, innovative and multidisciplinary approaches in teaching and learning; iv) facilitating the exchange, flow and co-creation of knowledge at a local and global level.
Gebouwautomatiseringssystemen voor de utiliteitssector zoals kantoren, scholen, ziekenhuizen vereisen steeds meer functionaliteit om tegemoet te komen aan nieuwe eisen en wensen van gebouwbeheer en eindgebruikers op gebied van o.a. comfort, bezetting, onderhoud interieur, afvalbeheer, energie en dergelijke. De recente technologische ontwikkelingen maken het mogelijk om de gebouwbeheersystemen in te zetten voor innovatieve toepassingen. Maar door lastige toegankelijkheid van bestaande systemen kunnen gebouwbeheerders onvoldoende gebruik maken van deze vernieuwingen. Fabrikanten van gebouwbeheersystemen (GBS) hebben hun producten (vaak op basis van BACnet) veelal zo ingericht dat onderlinge competitie en vrije marktwerking voor verschillende vernieuwende elementen op gebied van digitalisering van beheer- en onderhoudstaken moeilijk is. Recente ontwikkelingen maken het mogelijk binnen de field layer van BACnet dat nieuwe devices aan het bestaande gebouwbeheersysteem gekoppeld kunnen worden en reeds bestaande devices kunnen worden aangestuurd. Nieuwe open source data-mining applicaties (bijv. van Rapid Miner, IBM, Oracle) bieden daarbij de mogelijkheid nieuwe gegevens te genereren om het beheer van gebouwen verder te optimaliseren. Deze ontwikkelingen maken de weg vrij voor verdere toepassingen en innovaties en bieden kansen voor betrokken bedrijven in deze sector. Echter, gebouwbeheerders en installateurs zijn nog onwetend of onzeker van de mogelijkheden m.b.t. prestaties, robuustheid, integreerbaarheid en ondersteuning terwijl de behoefte tot nieuwe diensten groeit. In dit KIEM project wordt met een consortium van een sensor/ICT-ontwikkelbedrijf (Octo), een totaal installateur (E+W) (Lomans Amersfoort), een gebouwbeheerder (HU bedrijfsvoering) en drie onderzoekers uit verschillende lectoraten van de hogeschool Utrecht verkend welke open source datamining tools en innovatieve sensorsystemen van belang kunnen zijn voor de huidige gebouwautomatisering. Er wordt verkend waar de knelpunten zijn en waar de kansen liggen tot integratie. Daarbij kan gedacht worden aan diensten op basis van gebouwbeheer zoals gegarandeerd comfortabel binnenklimaat, efficiënte bezettingsgraad van ruimtes, vernieuwend afvalbeheer en optimale energiehuishouding. Maar ook andere potentiële diensten zullen verder worden onderzocht samen met ketenpartners en ICT/sensorsysteem-innovators. Deze verkenningen worden vertaald naar een programma voor vervolgonderzoek.
