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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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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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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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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Dealing with and maintaining high-quality standards in the design and construction phases is challenging, especially for on-site construction. Issues like improper implementation of building components and poor communication can widen the gap between design specifications and actual conditions. To prevent this, particularly for energy-efficient buildings, it is vital to develop resilient, sustainable strategies. These should optimize resource use, minimize environmental impact, and enhance livability, contributing to carbon neutrality by 2050 and climate change mitigation. Traditional post-occupancy evaluations, which identify defects after construction, are impractical for addressing energy performance gaps. A new, real-time inspection approach is necessary throughout the construction process. This paper suggests an innovative guideline for prefabricated buildings, emphasizing digital ‘self-instruction’ and ‘self-inspection’. These procedures ensure activities impacting quality adhere to specific instructions, drawings, and 3D models, incorporating the relevant acceptance criteria to verify completion. This methodology, promoting alignment with planned energy-efficient features, is supported by BIM-based software and Augmented Reality (AR) tools, embodying Industry 4.0 principles. BIM (Building Information Modeling) and AR bridge the gap between virtual design and actual construction, improving stakeholder communication and enabling real-time monitoring and adjustments. This integration fosters accuracy and efficiency, which are key for energy-efficient and nearly zero-energy buildings, marking a shift towards a more precise, collaborative, and environmentally sensible construction industry.
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Urban regions are confronted with huge sustainability challenges. Their future depends to a large extent on our ability to promote sustainable urban development. However, sustainability challenges in cities are inherently complex and need integrated, multidisciplinary solutions. This textbook on Smart Sustainable Cities responds to that challenge by capturing theories, methods and tools relevant for researching smart sustainable cities and developing solutions for sustainability challenges within cities. This book thereby serves the great need among students and practitioners to understand the multifaceted nature of Smart Sustainable Cities, to build upon acknowledged cross-disciplinary analytical and design approaches, and to learn how to apply such approaches. Each chapter presents a practical approach to urban sustainability, a relevant case study, and exercises and assignments for students to master the topic. Topics include: Smart Sustainable Cities: an introduction; Systemic Design Thinking; Probing the Future for Smart Sustainable Cities; Social Design of Smart Sustainable Cities; Urban Psychology of Smart Sustainable Cities; Behavioural Change for Smart Sustainable Cities; Healthy Urban Living; Towards Energy Neutral Neighbourhoods; Carbon Footprinting and Accounting; Circular Economy: material and value flows in the city; Promoting Sustainable Urban Mobility; Canvas Business Modelling; Big Data Analytics; Social Value Innovation: from concept to practice.
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The role of smart cities in order to improve older people’s quality of life, sustainability and opportunities, accessibility, mobility, and connectivity is increasing and acknowledged in public policy and private sector strategies in countries all over the world. Smart cities are one of the technological-driven initiatives that may help create an age-friendly city. Few research studies have analysed emerging countries in terms of their national strategies on smart or age-friendly cities. In this study, Romania which is predicted to become one of the most ageing countries in the European Union is used as a case study. Through document analysis, current initiatives at the local, regional, and national level addressing the issue of smart and age-friendly cities in Romania are investigated. In addition, a case study is presented to indicate possible ways of the smart cities initiatives to target and involve older adults. The role of different stakeholders is analysed in terms of whether initiatives are fragmentary or sustainable over time, and the importance of some key factors, such as private–public partnerships and transnational bodies. The results are discussed revealing the particularities of the smart cities initiatives in Romania in the time frame 2012–2020, which to date, have limited connection to the age-friendly cities agenda. Based on the findings, a set of recommendations are formulated to move the agenda forward. CC-BY Original article: https://doi.org/10.3390/ijerph17145202 (This article belongs to the Special Issue Feature Papers "Age-Friendly Cities & Communities: State of the Art and Future Perspectives") https://www.dehaagsehogeschool.nl/onderzoek/lectoraten/details/urban-ageing#over-het-lectoraat
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At this moment, charging your electric vehicle is common good, however smart charging is still a novelty in the developing phase with many unknowns. A smart charging system monitors, manages and restricts the charging process to optimize energy consumption. The need for, and advantages of smart charging electric vehicles are clear cut from the perspective of the government, energy suppliers and sustainability goals. But what about the advantages and disadvantages for the people who drive electric cars? What opportunities are there to support the goals of the user to make smart charging desirable for them? By means of qualitative Co-design methods the underlying motives of early adaptors for joining a smart charging service were uncovered. This was done by first sensitizing the user about their current and past encounters with smart charging to make them more aware of their everyday experiences. This was followed by another generative method, journey mapping and in-depth interviews to uncover the core values that drove them to participate in a smart charging system. Finally, during two co-design sessions, the participants formed groups in which they were challenged to design the future of smart charging guided by their core values. The three main findings are as follows. Firstly, participants are looking for ways to make their sustainable behaviour visible and measurable for themselves. For example, the money they saved by using the smart charging system was often used as a scoreboard, more than it was about theactual money. Secondly, they were more willing to participate in smart charging and discharging (sending energy from their vehicle back to the grid) if it had a direct positive effect on someone close to them. For example, a retiree stated that he was more than willing to share the energy of his car with a neighbouring family in which both young parents work, making them unable to charge their vehicles at times when renewable energy is available in abundance. The third and last finding is interrelated with this, it is about setting the right example. The early adopters want to show people close to them that they are making an effort to do the right thing. This is known as the law of proximity and is well illustrated by a participant that bought a second-hand, first-generation Nissan Leaf with a range of just 80 km in the summer and even less in winter. It isn’t about buying the best or most convenient car but about showing the children that sometimes it takes effort to do the right thing. These results suggest that there are clear opportunities for suppliers of smart EV charging services to make it more desirable for users, with other incentives than the now commonly used method of saving money. The main takeaway is that early adopters have a desire for their sustainable behaviour to be more visible and tangible for themselves and their social environment. The results have been translated into preliminary design proposals in which the law of proximity is applied.
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In ESSENCE (European Sustainable Solutions for Existing and New City Environments) "five European Higher Education Institutions and three municipalities worked together to train future professionals to overcome the complex challenges of achieving smart sustainable cities. Students worked on behalf of the three local governments on useful solutions to sustainability issues in the urban environment. New teaching methods were applied, such as blended learning and creative solution searching methods. "
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