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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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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From a circular standpoint it is interesting to reuse as much as possible construction and demolition waste (CDW) into new building projects. In most cases CDW will not be directly reusable and will need to be processed and stored first. In order to turn this into a successful business case CDW will need to be reused on a large scale. In this paper we present the concept of a centralized and coordinated location in the City of Utrecht where construction and demolition waste is collected, sorted, worked, stored for reuse, or shipped elsewhere for further processing in renewed materials. This has expected advantages for the amount of material reuse, financial advantages for firms and clients, generating employability in the logistics and processing of materials, optimizing the transport and distribution of materials through the city, and thus the reduction of emissions and congestion. In the paper we explore the local facility of a Circular Hub, and the potential effects on circular reuse, and other effects within the City of Utrecht.
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Limited data is available on the size of urban goods movement and its impact on numerous aspects with respect to livability such as emissions and spatial impact. The latter becomes more important in densifying cities. This makes it challenging to implement effective measures that aim to reduce the negative impact of urban good movement and to monitor their impact. Furthermore, urban goods movement is diverse and because of this a tailored approach is required to take effective measures. Minimizing the negative impact of a heavy truck in construction logistics requires a different approach than a parcel delivery van. Partly due to a lack of accurate data, this diversity is often not considered when taking measures. This study describes an approach how to use available data on urban traffic, and how to enrich these with other sources, which is used to gain insight into the decomposition (number of trips and kilometers per segment and vehicle type). The usefulness of having this insight is shown for different applications by two case studies: one to estimate the effect of a zero-emission zone in the city of Utrecht and another to estimate the logistics requirements in a car-free area development.
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Ending subsidies for fossil fuel heating systems from 2025, and phasing out gas boilers and other fossil fuel heaters by 2040. These are just two of the outcomes of a political agreement between the EU Council and the European Parliament, which was reached on December 7, 2023. Which measures were agreed upon, and what will the implications be for the heating sector?
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A replacement of cars with conventional internal combustion engines (ICEs) by electric vehicles (EVs) is seen by many as a means to improve local air quality, reduce dependence on fossil fuels and CO2 emissions. The market for EV is slowly developing with a growing number of (subsidized) manufacturers offering EV models in different market segments to (subsidized) car owners. The number of EVs is still small in most countries, but policymakers and manufacturers see partial or even full replacement of ICEs by EVs as realistic in the coming decade. EV engines are powered by rechargeable lithium-ion batteries. Li-ion is produced from precursors, either liquid (brine metal salt) or solid (hard rocks). Lithium mining is still concentrated in a few countries. Lithium is used for batteries, ceramics, grease and medicine. This reliance comes at a cost, as conventional lithium mining creates several externalities. The following main question will be addressed: How to source a required volume of lithium in a way that reduces the environmental and social-economic impact of mining this resource? To address this question, we will use a combination of relevant literature and a local case study supported by a model-based estimation. The focus is on the Netherlands, an EV user country, but the approach is generic.
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The Utrecht SBE16 Conference. From the introduction: "The conference is part of the Sustainable Built conference series and is as such considered to be part of the pre-eminent international conference series on sustainable building and construction endorsed by iiSBE, UNEP-SBO and FIDIC. The Utrecht SBE16 conference is hosted by the Centre of Expertise Smart Sustainable Cities of HU University of Applied Sciences Utrecht, in partnership with six Dutch Universities of Applied Sciences (Avans, Saxion, Rotterdam, The Hague, Zuyd, InHolland) and the Utrecht Sustainability Institute (USI). The Transition Zero conference provides us with a unique opportunity to meet transition professionals in urban sustainability from all over Europe and beyond and to learn about the latest developments and best (inter)national practices in urban sustainability. The rich interest in the conference, made it possible to offer research as well as practitioner-driven tracks on topics related to the conference title. The conference brought together excellent future-minded practitioners, researchers and thought leaders from the R&I community, specialists and professionals on zero energy homes and transition of the built environment."
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Uit voorwoord Anton Franken, lid CvB `Smart Sustainable Cities is een platform voor het bedrijfsleven, kennisinstellingen en Hogeschool Utrecht waar gezamenlijk vernieuwende producten en diensten worden ontwikkeld die de realisatie van slimme, duurzame en gezonde steden dichterbij brengt. Startende en ervaren professionals hebben hiermee de mogelijkheid om via het onderwijs of via bij- en nascholing de nieuwste toepasbare kennis en inzichten op dit gebied op te doen. Tevens verricht het platform onderzoek. In projecten werken studenten, bedrijven, docenten en onderzoekers samen om nieuwe kennis en inzichten tot toepassing te brengen. Drie inhoudelijke thema’s staan centraal: ‘Stedelijke gebieden energieneutraal’, ‘Gezonde gebieden gezond gebouwd’ en ‘Duurzaam gedrag: mens en organisatie’ .`
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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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The urgency for developing a circular economy is growing, and more and more companies and organisations are concerned with the importance of adapting their business to fit a changing economy. However, many analyses on the circular economy are still rather abstract and there is a lack of understanding about what circularity would mean for specific industries. This insufficient insight especially seems to be apparent in the building and construction sector. Besides, the building and construction sector is responsible for a major part of energy use and emissions. To tackle the issue of insufficient insight into the business consequences of circular developments, further research is necessary. Therefore, we propose to collaborate on a research project that aims to provide a more detailed level of analysis. The goal is to identify drivers and barriers to make better use of materials in the building and construction sector. This further research would benefit from an international collaboration between universities of applied sciences and industry from different European countries. An additional benefit of the applied orientation would be the relevance for professional education programmes. The article is published in the proceedings of the conference : http://dx.doi.org/10.4995/CARPE2019.2019.10582 Publisher Editorial Universitat Politècnica de València, 2019 www.lalibreria.upv.es / Ref.: 6523_01_01_01 Creative Commons Atribution-NonCommercial-NonDetivates-4.0 Int.
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