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 use of growth monitoring and promotion (GMP) has become widespread. It is a potential contributor towards achieving the Millennium Development Goals of halving hunger and reducing child mortality by two-thirds within 2015. Yet, GMP appears to be a prerequisite for good child health but several studies have shown that there is a discrepancy between the purpose and the practice of GMP. The high prevalence of malnutrition in many developing countries seems to confirm this fact. A descriptive qualitative study was carried out from April to September 2011. Focus group discussions and in-depth interviews were conducted amongst mothers and health workers. Data were analyzed using a qualitative content analysis technique, with the support of ATLAS.ti 5.0 software. The results suggest that most mothers were aware of the need for regular weight monitoring while health workers also seemed to be well-aware and to practise GMP according to the international guidelines. However, there was a deficit in maternal knowledge with regard to child-feeding and a lack of basic resources to keep and/or to buy healthful and nutritionally-rich food. Furthermore, the role of the husband was not always supportive of proper child-feeding. In general, GMP is unlikely to succeed if mothers lack awareness of proper child-feeding practices, and if they are not supported by their husbands.
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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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An overview of innovations in a particular area, for example retail developments in the fashion sector (Van Vliet, 2014), and a subsequent discussion about the probability as to whether these innovations will realise a ‘breakthrough’, has to be supplemented with the question of what the added value is for the customer of such a new service or product. The added value for the customer must not only be clear as to its direct (instrumental or hedonic) incentives but it must also be tested on its merits from a business point of view. This requires a methodology. Working with business models is a method for describing the added value of products/services for customers in a systematic and structured manner. The fact that this is not always simple is evident from the discussions about retail developments, which do not excel in well-grounded business models. If there is talk about business models at all, it is more likely to concern strategic positioning in the market or value chain, or the discussion is about specifics like earning- and distribution-models (see Molenaar, 2011; Shopping 2020, 2014). Here we shall deal with two aspects of business models. First of all we shall look at the different perspectives in the use of business models, ultimately arriving at four distinctive perspectives or methods of use. Secondly, we shall outline the context within which business models operate. As a conclusion we shall distil a research framework from these discussions by presenting an integrated model as the basis for further research into new services and product.
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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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Here is something that all Europeans find of prime importance: affordable access to good health care; high quality elderly care; being able to live independently, even if you are handicapped or chronically ill.
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The paper explores the process of early growth of entrepreneurial science-based firms. Drawing on case studies of British and Dutch biopharmaceutical R&D firms, we conceptualize the speed of early growth of science-based firms as the time it takes for the assembly (or combined development) of three types of critical resources - a functionally-diverse management team, early fundraising and development of technology. The development of these resources is an unfolding and interrelated process, the causal direction of which is highly ambiguous. We show the variety of paths used by science-based firms to access and develop these critical resources. The picture that emerges is that the various combinations of what we call "assisted" and "unassisted" paths combine to influence the speed of firm growth. We show how a wide range of manifestations of technology development act as signaling devices to attract funding and management, affecting the speed of firm development. We also show how the variety of paths and the speed of development are influenced by the national institutional setting.
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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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Amidst escalating environmental and social challenges, this study explores regenerative business models’ definition and characteristics. While sustainable models have made considerable strides in research, policy, and practice, the advent of regenerative business models offers a progressive leap forward. Regenerative business models aspire to contribute to ecological restoration and societal well-being. The regenerative business model concept is, however, still in its infancy and lacks a comprehensive definition. Our study aims to expand this knowledge, using a Delphi-inspired approach that builds on the knowledge of academic and business experts. Our approach includes three rounds of surveys: an open-ended survey, a survey for rating and ranking the earlier responses of all participants, and a final survey to select key characteristics. We investigate patterns and distinctions among regenerative, regenerative business, and regenerative business models, and analyze their positioning vis-a-vis circular and net-positive models. Findings underscore that organizations adopting regenerative business models focus on planetary health and societal well-being. They generate value across multiple stakeholder levels, including nature, societies, customers, suppliers, shareholders, and employees. Despite overlapping with circular and net-positive models, regenerative business models also emphasize interdependencies between humans and nature, and provide a more holistic approach, centered on restoration rather than mere mitigation.
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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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