The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
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Dit artikel beschrijft een onderzoek naar werkzame elementen in de samenwerking binnen innovatieve leeromgevingen, professionele werkplaatsen (PW) genoemd. In PW werken onderwijs en beroepspraktijk samen aan complexe vraagstukken waarbij de ontwikkeling van betrokkenen en de innovatie van de beroepspraktijk centraal staan. Op basis van literatuuronderzoek, verkennende interviews met 11 sleutelfiguren en een meervoudige casestudie waarin vanuit 4 cases 75 betrokkenen participeerden, is het model Lerend en Onderzoekend Samenwerken in PW ontwikkeld. Het model omvat zes elementen en laat zien dat het lerend en onderzoekend samenwerken centraal staat in een PW en zich ontwikkelt binnen een grensoverstijgende en ontwikkelingsgerichte cultuur. Betrokkenen in een PW leren gezamenlijk doordat ze samenwerken in de dienstverlening en hierbij waarde hechten aan het delen van verschillende perspectieven. Door facilitering van mensen en middelen en door de samenwerking vorm te geven vanuit een gezamenlijke visie, kunnen betrokkenen elkaar leren kennen en afstemmen op welke manier zij samen kunnen bijdragen aan de innovatie van de beroepspraktijk. Hiervoor zijn zowel het opbouwen van relaties als het expliciteren en verdelen van taken en verantwoordelijkheden essentieel. Het model, dat een systemisch perspectief kent, biedt uitgangspunten en handvatten om de samenwerking binnen een PW te evalueren en te versterken.
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The design of healthcare facilities is a complex and dynamic process, which involves many stakeholders each with their own set of needs. In the context of healthcare facilities, this complexity exists at the intersection of technology and society because the very design of these buildings forces us to consider the technology–human interface directly in terms of living-space, ethics and social priorities. In order to grasp this complexity, current healthcare design models need mechanisms to help prioritize the needs of the stakeholders. Assistance in this process can be derived by incorporating elements of technology philosophy into existing design models. In this article, we develop and examine the Inclusive and Integrated Health Facilities Design model (In2Health Design model) and its foundations. This model brings together three existing approaches: (i) the International Classification of Functioning, Disability and Health, (ii) the Model of Integrated Building Design, and (iii) the ontology by Dooyeweerd. The model can be used to analyze the needs of the various stakeholders, in relationship to the required performances of a building as delivered by various building systems. The applicability of the In2Health Design model is illustrated by two case studies concerning (i) the evaluation of the indoor environment for older people with dementia and (ii) the design process of the redevelopment of an existing hospital for psychiatric patients.
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The economic recession has hit especially hard the residential building sector in the EU region, e.g., the number of the housing completions has decreased -49% and the total residential output has been squeezed down by -24% between 2007 and 2014 (Euroconstruct, 2015). In turn, the aim of our paper is to suggest a set of radical, novel programmes for developing the national residential building sectors within EU member countries up to 2025. We have applied the framework of strategic niche management (SNM) to the diagnoses of the current portfolios of the innovation, R&D programs in our two member country contexts. In the case of the Northern Finland, the prime example is Hiukkavaara, the largest district to be built in the City of Oulu. Homes will be constructed for 20,000 new residents. Hiukkavaara is a model for climate- conscious design in the northern hemisphere. Energy and materials are conserved, nature is valued and human beings adapt to their environment. One sub-programme involves Future Buildings and Renewable Energy Project. In the case of the Netherlands, the prime example is Energiesprong (Energy Leap), i.e., the innovation programme commissioned by the Dutch Ministry of the Interior. The aim is to make buildings energy-neutral and boost large-scale initiatives. The sub-programmes are targeting homes owned by housing associations, privately owned homes, office buildings, shops and care institutions. This programme is about ensuring new supply by encouraging companies to package a variety of technical sub-solutions, full services and financing options as well as about asking clients to put out tenders and ask for quotes in novel ways, with the government making changes to the rules and the regulations. Experiences on which the Dutch case in this paper focuses are sub-programmes for residential buildings, which include de Stroomversnelling, LALOG and Ons Huis Verdient Het. Based on the emerging Finnish and Dutch evidence, we are suggesting key elements to be incorporated into future national residential programmes within EU member countries on: (1) radical direction with balanced stakeholder groups, trustworthy advocates, contextual goal-setting and barriers management, (2) radical networking with entrepreneurial roles and causal links, novel expertise, transparent choices and digital platforms and (3) radical learning processes to arrive at better informed markets on user preferences, co-innovating, new rules and regulations, higher performance/price ratios, higher quality, new roles and responsibilities assignments.
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Buildings need to be carefully operated and maintained for optimum health, comfort, energy performance, and utility costs. The increasing use of Machine Learning combined with Big Data in the building services sector has shown the potential to bring energy efficiency and cost-effectiveness. Therefore, upskilling and reskilling the current workforce is required to realize new possibilities. In addition, sharing and preserving knowledge are also required for the sustainable growth of professionals and companies. This formed the basis for the Dutch Research Council funded TransAct project. To increase access to education on the job, online learning is experiencing phenomenal growth. A study was conducted with two focus groups - professionals of a building service company and university researchers - to understand the existing challenges and the ways to improve knowledge sharing and upskilling through learning on the job. This study introduced an Enterprise Social Network platform that connects members and may facilitate knowledge sharing. As a community forum, Yammer from office 365 was used. For hosting project files, a SharePoint page was created. For online courses, the company’s online learning site was utilized. The log data from the online tools were analysed, semi-structured interviews and webinars were conducted and feedback was collected with google forms. Incentive models like social recognition and innovative project results were used to motivate the professionals for online activities. This paper distinguishes the impacts of initiatives on the behaviour of university researchers vs company employees.
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This study explores the evaluation of research pathways of self-management health innovations from discovery to implementation in the context of practice-based research. The aim is to understand how a new process model for evaluating practice-based research provides insights into the implementation success of innovations. Data were collected from nine research projects in the Netherlands. Through document analysis and semi-structured interviews, we analysed how the projects start, evolve, and contribute to the healthcare practice. Building on previous research evaluation approaches to monitor knowledge utilization, we developed a Research Pathway Model. The model’s process character enables us to include and evaluate the incremental work required throughout the lifespan of an innovation project and it helps to foreground that innovation continues during implementation in real-life settings. We found that in each research project, pathways are followed that include activities to explore a new solution, deliver a prototype and contribute to theory. Only three projects explored the solution in real life and included activities to create the necessary changes for the solutions to be adopted. These three projects were associated with successful implementation. The exploration of the solution in a real-life environment in which users test a prototype in their own context seems to be a necessary research activity for the successful implementation of self-management health innovations.
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Urban densification is a key strategy to accommodate rapid urban population growth, but emerging evidence suggests serious risks of urban densification for individuals’ mental health. To better understand the complex pathways from urban densification to mental health, we integrated interdisciplinary expert knowledge in a causal loop diagram via group model building techniques. Six subsystems were identified: five subsystems describing mechanisms on how changes in the urban system caused by urban densification may impact mental health, and one showing how changes in mental health may alter urban densification. The new insights can help to develop resilient, healthier cities for all.
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For delayed and long-term students, the education process is often a lonely journey. The main conclusion of this research is that learning should not be an individual process of the student connected to one lecturer, but rather a community where learning is a collective journey. The social interaction between lecturers, groups of delayed students and other actors is an important engine for arriving at the new knowledge, insights and expertise that are important to reach their final level. This calls for the design of social structures and the collaboration mechanism that enable the bonding of all members in the community. By making use of this added value, new opportunities for the individual are created that can lead to study success. Another important conclusion is that in the design and development of learning communities, sufficient attention must be paid to cultural characteristics. Students who delay are faced with a loss of self-efficacy and feelings of shame and guilt. A learning community for delayed students requires a culture in which students can turn this experience into an experience of self-confidence, hope and optimism. This requires that the education system pays attention to language use, symbols and rituals to realise this turn. The model ‘Building blocks of a learning environment for long-term students’ contains elements that contribute to the study success of delayed and long-term students. It is the challenge for every education programme to use it in an appropriate way within its own educational context. Each department will have to explore for themselves how these elements can be translated into the actions, language, symbols and rituals that are suitable for their own target group.
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Virtual communities are online spaces with potential of integration of (member-generated) content and conversations [7,8]. In our research project we are interested in the adoption and building of virtual communities in organized sports, that is to say in the voluntary sports clubs (VSCs) in the Netherlands. Since these VSCs have massively transferred their communication with members from paper club magazines to online channels, these virtual communities arise from the use of a growing number of websites, e-mail and social network sites (SNSs). Although virtual communities are broadly investigated, such as social communities, brand communities, and public communities, there is little scholarly interest in virtual communities of member organizations that VSCs are an example of. The study that is to be presented at SECSI 2019 concerns the clubs’ use of SNSs (ClubSNSs), such as Facebook and Twitter, within the virtual communities. These SNSs are increasingly used by the VSCs to facilitate organizational communication and to obtain a good internal climate [9]. However, academic understanding of the impact of ClubSNSs’ content and conversations on the organizational performance of the VSC is in its infancy. In our study, we examined this impact of ClubSNSs use on the involvement among members and whether we can explain this by members’ identification with the club. Furthermore, we have tried to categorize ClubSNSs by content types, such as informative, conversational or sociable ClubSNSs, and their role in stimulating the use of ClubSNSs. In this way we attempted to gain insight into the effect of types of ClubSNSs’ content and conversations on membership involvement and the mediating role of identification with the club. This insight can help VSCs to develop effective ClubSNS channels that contribute to organizational goals such as supportive and loyal membership.
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The BMT provides the building blocks to develop a logic for a business model. In such a model the nature of value creation, how value creation is organized, and how transactions are taking shape are operationalized so that they meet the proposition. Practice shows that at present business models aimed at capturing multiple value creation can be divided into three major categories: (1) platform business models, (2) community-based (or collective) business models, and (3) circular business models. The three archetypes differ mainly in the way in which they create value, as well as the objective, the mechanism through which value creation takes place, and the infrastructural and technological requirements. When using the BMT, it is useful to consider at an early stage which business model archetype is dominant in the realization of the intended value proposition. Choosing a business model archetype might look straightforward, but it can be quite a tricky task.
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