High-tech horticulture production methods (such as vertical farming, hydroponics and other related technology possibilities), combined with evolving market side possibilities (consumer’s willingness to pay for variety, food safety and security), are opening new ways to create and deliver value. In this paper we present four emerging business models and attempt to understand the conditions under which each business model is able to create positive market value and sustained business advantage. The first of these four models is the case of a vertically integrated production to retail operation. The second model is the case of a production model with assured retail/distribution side commitment. The third model deals with a marketing/branding driven production model with differentiated market positioning. Finally, the forth is a production model with direct delivery to the end-consumer based upon the leveraging of wide spread digital technology in the consumer market. To demonstrate these four business models, we analyze practical case studies and analyze their market approach and impact. Using this analysis, we create a framework that enables entrepreneurs and businesses to adopt a business model that matches their capabilities with market opportunities.
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Wanneer je met je lessen specifieke leerdoelen hebt, zoals tactisch inzicht, regelvaardigheden, of samenwerking, dan hoort daar ook een specifieke aanpak bij. De laatste jaren zijn er in de LO, met name in het buitenland, verschillende modellen ontwikkeld die je hierbij kunnen helpen. Deze aanpak wordt ook wel 'Models-Based Physical Education genoemd”.
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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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Research, advisory companies, consultants and system integrators all predict that a lot of money will be earned with decision management (business rules, algorithms and analytics). But how can you actually make money with decision management or in other words: Which business models are exactly available? In this article, we present seven business models for decision management.
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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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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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This Whitepaper presents the essence of research into existing and emerging circular business models (CBMs). This results in the identification of seven basic types of CBM, divided into three groups that together form a classification. This Whitepaper consists of three parts.▪ The first part discusses the background and explains the circular economy (CE), the connection with sustainability, business models and an overview of circular business models.▪ In the second part, an overview is given of the developed classification of CBM, and each basic type is described based on its characteristics. This has resulted in seven knowledge maps. Finally, the last two, more future-oriented models are further explained and illustrated.▪ The third part looks back briefly at the reliability of the classification made and then at the aspects of change management in working on and with a CBM.
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There are substantial differences between models of the economic impacts of tourism. Not only do the nature and precision of results vary, but data demands, complexity and underlying assumptions also differ. Often, it is not clear whether the models chosen are appropriate for the specific situation to which they are applied. The goal of this article is to provide an overview and evaluation of criteria for the selection of economic impact models. A literature review produced 52 potential criteria, subdivided into 10 groups. Based on an analysis of experts' opinions, the perceived importance of each criterion was determined and a set of essential criteria created. To illustrate the usage of these essential criteria, five models (export base, Keynesian, ad hoc, input-output and computable general equilibrium) were evaluated and compared based on their performance on these criteria. This paper builds on the existing literature by showing that it is possible to make a more informed choice among economic impact models of tourism.
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Students often struggle with constructing models of system behaviour, particularly in open modelling tasks where there is no single correct answer. The challenge lies in providing effective support that helps students develop high quality models while maintaining their autonomy in the modelling process. This study presents a procedure for assessing the quality of student-generated qualitative models in open modelling tasks, based on three characteristics: correctness, parsimony, and completeness. The procedure was developed and refined using student-generated models from two secondary school tasks on thermoregulation and sound properties. The findings contribute to the development of automated support systems that guide students through open modelling tasks by focusing on quality characteristics rather than adherence to a predefined norm model.
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This report describes the Utrecht regio with regard to sustainability and circular business models.
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