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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From November 2013 till January 2014 a minor ‘Smart Life Rhythms’ was taught at The Hague University of Applied Sciences. In the minor students used service design methods to develop solutions for improving life rhythms. Reflection on the minor produced the insight that building physical prototypes early on in the design process was key to success. Further discussions with colleagues and a literature review gave more arguments for the motto ‘Just build it’ – an encouragement to build simple physical models in the early stages of the service design process. Building these simple physical models is not just advocated by educators and in line with service design principles such as being iterative and user-centered. In his book ‘the Craftsman’ (Sennett, 2009) Richard Sennett provides us with more fundamental arguments regarding the value of ‘making things’. On top of the added value to the design process in itself, simple physical models are a tool for engaging both clients, users and students in the design process. So get out your glue gun and start building!
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-Chatbots are being used at an increasing rate, for instance, for simple Q&A conversations, flight reservations, online shopping and news aggregation. However, users expect to be served as effective and reliable as they were with human-based systems and are unforgiving once the system fails to understand them, engage them or show them human empathy. This problem is more prominent when the technology is used in domains such as health care, where empathy and the ability to give emotional support are most essential during interaction with the person. Empathy, however, is a unique human skill, and conversational agents such as chatbots cannot yet express empathy in nuanced ways to account for its complex nature and quality. This project focuses on designing emotionally supportive conversational agents within the mental health domain. We take a user-centered co-creation approach to focus on the mental health problems of sexual assault victims. This group is chosen specifically, because of the high rate of the sexual assault incidents and its lifetime destructive effects on the victim and the fact that although early intervention and treatment is necessary to prevent future mental health problems, these incidents largely go unreported due to the stigma attached to sexual assault. On the other hand, research shows that people feel more comfortable talking to chatbots about intimate topics since they feel no fear of judgment. We think an emotionally supportive and empathic chatbot specifically designed to encourage self-disclosure among sexual assault victims could help those who remain silent in fear of negative evaluation and empower them to process their experience better and take the necessary steps towards treatment early on.
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.
Worldwide, a third of all adults is suffering from feelings of loneliness, with a peak at young adulthood (15-25 years old). Loneliness has serious consequences for mental and physical health and should therefore be urgently addressed. However, existing interventions targeting loneliness mainly focus on older adults [1], and rarely consider the physical living environment, while studies prove that the physical environment (e.g. amenities, green, walkability, liveliness) has a significant impact on loneliness. Collaboration between the psychosocial and physical domains is key, to gain insight into the mechanisms and pathways linking characteristics of the physical living environment and loneliness among young adults and which spatial interventions are effective in managing loneliness. The main research questions are thus: how are physical environment and loneliness related, and which interventions should be implemented? The I BELONG proposal aims to build a European consortium that will address these questions. WP1 encompasses collaboration and networking activities that will form the basis for future collaboration, for instance a European research grant application. WP2 will provide insight in the pathways linking spatial attributes and loneliness. This will be achieved by doing a systematic literature review, a photovoice and interview study to collect data on specific locations that affect young people’s experiences with loneliness, and Group Model Building with experts. Building on this, WP3 aims to co-create spatial interventions with partners and young adults, and test ‘proof of concept’ interventions with virtual environments among young adults. WP3 will result in a spatial intervention toolkit. This project has both societal and scientific impact, as it will provide knowledge on pathways between physical environment characteristics and feelings of loneliness among young people, evidence of what spatial interventions work, and design guidelines that can be used in urban design and management that can contribute to managing loneliness and related health risks.
