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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Due to a number of factors outlined in this article, the issue of population growth is excluded from the sustainability discussion. In this article, we explore some of the ethical presumptions that underlie the issues linking population growth and sustainability. Critics argue that action to address population creates social and economic segregation, and portray overpopulation concerns as being “anti-poor,” “anti-developing country,” or even “antihuman.” Yet, de-linking demographic factors from sustainability concerns ignores significant global realities and trends, such as the ecological limits of the Earth, the welfare and long-term livelihood of the most vulnerable groups, future prospects of humanity, as well as the ecosystems that support society. https://doi.org/10.1080/10042857.2016.1149296 LinkedIn: https://www.linkedin.com/in/helenkopnina/
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The Dutch greenhouse horticultural industry is characterized by world leadership in high-tech innovation. The dynamics of this playing field are innovation in production systems and automation, reduction in energy consumption and sharing limited space. However, international competitive advantage of the industry is under pressure and sustainable growth of individual enterprises is no longer a certainty. The sector's ambition is to innovate better and grow faster than the competition in the rest of the world. Realizing this ambition requires strengthening the knowledge base, stimulating entrepreneurship, innovation (not just technological, but especially business process innovation). It also requires educating and professionalizing people. However, knowledge transfer in this industry is often fragmented and innovation through collaboration takes up a mere 25-30% of the opportunities. The greenhouse horticulture sector is generally characterized by small scale, often family run businesses. Growers often depend on the Dutch auction system for their revenues and suppliers operate mainly independently. Horizontal and vertical collaboration throughout the value chain is limited. This paper focuses on the question: how can the grower and the supplier in the greenhouse horticulture chain gain competitive advantage through radical product and process innovation. The challenge lies in time- to-market, in customer relationship, in developing new product/market combinations and in innovative entrepreneurship. In this paper an innovation and entrepreneurial educational and research programme is introduced. The programme aims at strengthening multidisciplinary collaboration between enterprise, education and research. Using best practice examples, the paper illustrates how companies can realize growth and improve innovative capabilities of the organization as well as the individual by linking economic and social sustainability. The paper continues to show how participants of the programme develop competencies by means of going through a learning cycle of single-loop, double-loop and triple loop learning: reduction of mistakes, change towards new concepts and improvement of the ability to learn. Furthermore, the paper discusses our four-year programme, whose objectives are trying to eliminate interventions that stimulate the innovative capabilities of SME's in this sector and develop instruments that are beneficial to organizations and individual entrepreneurs and help them make the step from vision to action, and from incremental to radical innovation. Finally, the paper illustrates the importance of combining enterprise, education and research in networks with a regional, national and international scope, with examples from the greenhouse horticulture sector. These networks generate economic regional and national growth and international competitiveness by acting as business accelerators.
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In our in-depth case study on two circular business models we found important roles for material scouts and networks. These key partners are essential for establishing circular business models and circular flow of materials. Besides, we diagnose that companies are having difficulties to develop viable value propositions and circular strategies. The paper was presented at NBM Nijmegen 2020 and will be published at a later date
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The authors consider the reality that endless economic growth on a finite planet is unsustainable, especially if society has exceeded ecological limits. The paper examines various aspects of society's endless growth predicament. It reviews the idea that there are 'limits to growth'; it then considers the 'endless growth mantra' within society. The paper then considers the 'decoupling' strategy and its merits, and argues that it is, at best, a partial solution to the problem. The key social problem of denial of our predicament is considered, along with the contribution of anthropocentric modernism as a worldview that aids and abets that denial. Finally, the paper outlines some potential solutions to our growth predicament. https://www.ecologicalcitizen.net/article.php?t=insanity-endless-growth https://www.linkedin.com/in/helenkopnina/
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This report describes the Utrecht regio with regard to sustainability and circular business models.
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Young widowhood, conceptualized as the loss of one’s spouse before the age of 50, is a profoundly painful and distressing loss (Den Elzen, 2017, 2018). The literature on young widowhood shows the death of a partner generally causes a fragmentation of the self, as it violates expectations of the normal life cycle, namely growing old together (Haase and Johnston, 2012; Levinson, 1997). Premature loss of one’s spouse tends to be experienced by the surviving partner as distressing or traumatizing, such as having witnessed their suffering in illness or through accident (Den Elzen, 2018) or in struggling with unfinished business (Holland et al, 2020). Whilst post-traumatic stress is well-known and has been widely researched across various disciplines, the concept of post-traumatic growth is much newer and by contrast has received less attention. PTG was introduced as a scholarly concept by Tedeschi and Calhoun in the mid-1990s and is defined as a positive psychological change as a result of the struggle with highly challenging life events (2004). Calhoun and Tedeschi’s notion of PTG has been backed by a recent systematic review. In the first meta-analysis of moderate-to-high PTG, Wu et al. found that of the 10,181 subjects, about 50% experienced PTG (2019). They also reported that women, young people and victims of trauma experienced higher levels of PTG than men, the elderly and those who experienced indirect trauma. PTG has attracted some controversy, with some researchers questioning its scientific validity (Jayawickreme and Blackie, 2014). Others caution against the minimization of people’s suffering. Hayward is a trauma counsellor who advises approaching PTG carefully, highlighting that if it is introduced with clients too early it can "often be construed as minimizing someone's pain and suffering and minimizing the impact of the loss" (cited in Collier, 2016, n.p.). In addressing the critique of PTG, Calhoun and Tedeschi (2006) emphasize that the focus on investigating positive psychological change following trauma does not deny the common and well-documented negative psychological responses and distress following severe life stresses: “Negative events tend to produce, for most persons, consequences that are negative” (p.4). They argue however, and their research supports this finding, that for many people distressful events can foster positive psychological changes. We view PTG as a possibility following (profound) loss, and emphasize that PTG may continue to co-exist with painful and/or unresolved emotions regarding the loss itself. We conceptualize PTG as a continuum and not as an either/or binary with grief. Further, we wish to highlight that PTG is a highly individual process that depends on many factors, and we are not suggesting that the absence of PTG is to be seen as a failure. This chapter intends to contribute to the study of PTG through a person-centered approach. The most used method to assess PTG is the 21-item posttraumatic growth inventory developed by Calhoun and Tedeschi in 1996 (Jayawickreme & Blackie, 2014). Self-reported posttraumatic growth has been the foundation of PTG research, which has aimed to identify to what extent PTG evokes improved psychological and physical health. In discussing our own creative narrative processes of PTG, our practice-led-research lens aims to contribute to research on how PTG might be fostered. We propose a Writing-for-wellbeing approach in this context and explore what it offered us both as writers and widows and what it might offer the field of Writing-for-wellbeing and by extension clinical and scholarly practice.
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The number of Electric Vehicles (EVs) is expected to increase exponentially in the coming years. The growing presence of charging points generates a multitude of interactions between EV users, particularly in metropolitan areas where a charging infrastructure is largely part of the public domain. There is a current knowledge gap as to how current decisions on charging infrastructure deployment affect both current and future infrastructure performance. In the thesis an attempt is made to bridge this knowledge gap by creating a deeper understanding of the relation between charging behavior, charging infrastructure deployment, and performance.The results demonstrate shown how both strategic and demand-drive deployment strategies have an effect on performance metrics. In a case study in the Netherlands it was found that during the initial deployment phase, strategic Charging Points (CPs) facilitate EV users better than demand driven deployment. As EV user adoption increased, demand-driven CPs show to outperform strategic CPs.This thesis further shows that there are 9 EV user types each with distinct difference distinct behavior in terms of charging frequency and mean energy uptake, both of which relate to aggregate CP performance and that user type composition, interactions between users and battery size play an important role in explaining performance of charging infrastructure.A validated data-driven agent-based model was developed to explore effects of interactions in the EV system and how they influence performance. The simulation results demonstrate that there is a non-linear relation between system utilization and inconvenience even at the base case scenario. Also, a significant rise of EV user population will lead to an occupancy of non-habitual charging at the expense of habitual EV users, which leads to an expected decline of occupancy for habitual EV users.Additional simulations studies support the hypothesis that several Complex Systems properties are currently present and affecting the relation between performance and occupation.
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According to the critics of conventional sustainability models, particularly within the business context, it is questionable whether the objective of balancing the social, economic and environmental triad is feasible, and whether human equality and prosperity (as well as population growth) can be achieved with the present rate of natural degradation (Rees 2009). The current scale of human economic activity on Earth is already excessive; finding itself in a state of unsustainable ‘overshoot’ where consumption and dissipation of energy and material resources exceed the regenerative and assimilative capacity of supportive ecosystems (Rees 2012). Conceptualizing the current ‘politics of unsustainability’, reflected in mainstream sustainability debates, Blühdorn (2011) explores the paradox of wanting to ‘sustain the unsustainable, noting that the socio-cultural norms underpinning unsustainability support denial of the gravity of our planetary crises. This denial concerns anything from the imminence of mass extinctions to climate change. As Foster (2014) has phrased it: ‘There was a brief window of opportunity when the sustainability agenda might, at least in principle, have averted it’. That agenda, however, has failed. Not might fail, nor even is likely to fail – but has already failed. Yet, instead of acknowledging this failure and moving on from the realization of the catastrophe to the required radical measures, the optimists of sustainable development and ecological modernization continue to celebrate the purported ‘balance' between people, profit and planet. This is an Accepted Manuscript of a book chapter published by Routledge/CRC Press in "A Future Beyond Growth: Towards a Steady State Economy" on 4/14/16 ,available online: https://doi.org/10.4324/9781315667515 LinkedIn: https://www.linkedin.com/in/helenkopnina/
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To enhance our understanding of forest carbon sequestration, climate change mitigation and drought impact on forest ecosystems, the availability of high-resolution annual forest growth maps based on tree-ring width (TRW) would provide a significant advancement to the field. Site-specific characteristics, which can be approximated by high-resolution Earth observation by satellites (EOS), emerge as crucial drivers of forest growth, influencing how climate translates into tree growth. EOS provides information on surface reflectance related to forest characteristics and thus can potentially improve the accuracy of forest growth models based on TRW. Through the modelling of TRW using EOS, climate and topography data, we showed that species-specific models can explain up to 52 % of model variance (Quercus petraea), while combining different species results in relatively poor model performance (R2 = 13 %). The integration of EOS into models based solely on climate and elevation data improved the explained variance by 6 % on average. Leveraging these insights, we successfully generated a map of annual TRW for the year 2021. We employed the area of applicability (AOA) approach to delineate the range in which our models are deemed valid. The calculated AOA for the established forest-type models was 73 % of the study region, indicating robust spatial applicability. Notably, unreliable predictions predominantly occurred in the climate margins of our dataset. In conclusion, our large-scale assessment underscores the efficacy of combining climate, EOS and topographic data to develop robust models for mapping annual TRW. This research not only fills a critical void in the current understanding of forest growth dynamics but also highlights the potential of integrated data sources for comprehensive ecosystem assessments.
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