The implementation of innovative sustainability technologies often requires far reaching changes of the macro environment in which the innovating firms operate. Strategic management literature describes that firms who want to commercialize an innovative technology can collaborate in networks or industry clusters to build up a favourable environment for their technology. This increases the chances of successful diffusion and adoption of the technology in society. However, the strategic management literature does not offer advice on how to strategically build up this supportive external environment. We fill this gap with complementary insights from the technological innovation systems literature. We introduce the concept of strategic collective system building. Collective system building describes processes and activities networks of actors can strategically engage in to collectively build up a favourable environment for their innovative sustainability technology. Furthermore, we develop a strategy framework for collective system building. To underpin the theoretical analysis empirically, we conducted a case study in the Dutch smart grids field. The resulting strategy framework consists of four key areas for strategy making: technology development and optimization, market creation, socio-cultural changes and coordination. Each of these key strategic areas is composed of a set of system building activities
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The building and construction industry, which is responsible for 39% of global carbon emissions, is far off track in achieving its net-zero emission targets. Product-service system (PSS) business models are one of the instruments used by the industry in the transition toward reaching these targets. A PSS business model is designed around an end-of-life solution that minimizes material usage and maximizes energy efficiency. It is provided to customers as a marketable set of products and services, jointly capable of fulfilling a customer’s needs. There are signals from practice however, that suggest that the implementation of this type of business model is falling behind. This study investigates this and seeks to identify key challenges and opportunities for sustainable PSS business models in the built environment. Using a grounded theory approach, data from 13 semi-structured interviews across five companies is used to identify challenges and opportunities that suppliers are facing in selling their products through PSS business models. Our preliminary data analysis points to nine challenges and opportunities for PSS business models. We discuss these in the context of the current economic transition toward a sustainable and circular built environment and provide suggestions for further research that could help to overcome resistance toward the implementation of PSS business models. The contribution of this research to researchers and practitioners is that it provides insights into the adoption of new business models in fragmented and competitive business environments.
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Societal one-way directed approaches of sustainable primary school building design cause persistent physical building problems. It affects the performances of the societal challenge of designing real sustainable school buildings, as well as the educational and social processes, and its end-user performances. Conventional building construction approaches build traditionally their designs on a syntheses of dialogues and consensus during decision-making processes, due to a variety of different interests. Principals define their ambitions and requirements into a team of mainly technical domain related disciplines. There are no design methods available that connect human systems and ecosystems integrated and balance the dynamic multi-level scaled mechanisms of human needs and sustainability development factors. The presented analytic framework recognizes similarity patterns between these multi-level scaled social systems, ecosystems and sustainable development entities, qualitatively as well as quantitatively. It delivers a new polarity based dynamic system that contributes to the client briefs and physical building morphological factors from a more sustainable development base. This theoretical approach establishes Sustainability-Centered Guidelines for primary schools (SCGs) design and building.
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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 energy management systems industry in the built environment is currently an important topic. Buildings use about 40% of the total global energy worldwide. Therefore, the energy management system’s sector is one of the most influential sectors to realize changes and transformation of energy use. New data science technologies used in building energy management systems might not only bring many technical challenges, but also they raise significant educational challenges for professionals who work in the field of energy management systems. Learning and educational issues are mainly due to the transformation of professional practices and networks, emerging technologies, and a big shift in how people work, communicate, and share their knowledge across the professional and academic sectors. In this study, we have investigated three different companies active in the building services sector to identify the main motivation and barriers to knowledge adoption, transfer, and exchange between different professionals in the energy management sector and explore the technologies that have been used in this field using the boundary-crossing framework. The results of our study show the importance of understanding professional learning networks in the building services sector. Additionally, the role of learning culture, incentive structure, and technologies behind the educational system of each organization are explained. Boundary-crossing helps to analyze the barriers and challenges in the educational setting and how new educational technologies can be embedded. Based on our results, future studies with a bigger sample and deeper analysis of technologies are needed to have a better understanding of current educational problems.
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Population ageing has become a domain of international discussions and research throughout the spectrum of disciplines including housing, urban planning, and real estate. Older people are encouraged to continue living in their homes in their familiar environment, and this is referred to as “ageing-in-place”. Enabling one to age-in-place requires new housing arrangements that facilitate and enable older adults to live comfortably into old age, preferably with others. Innovative examples are provided from a Dutch social housing association, illustrating a new approach to environmental design that focuses more on building new communities in conjunction with the building itself, as opposed to the occupational therapeutic approaches and environmental support. Transformation projects, referred to as “Second Youth Experiments”, are conducted using the Røring method, which is based on the principles of co-creation. De Benring in Voorst, The Netherlands, is provided as a case study of an innovative transformation project. This project shows how social and technological innovations can be integrated in the retrofitting of existing real estate for older people. It leads to a flexible use of the real estate, which makes the building system- and customer preference proof. Original article at: https://doi.org/10.3390/buildings8070089 © 2018 by the authors. Licensee MDPI.
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The Heating Ventilation and Air Conditioning (HVAC) sector is responsible for a large part of the total worldwide energy consumption, a significant part of which is caused by incorrect operation of controls and maintenance. HVAC systems are becoming increasingly complex, especially due to multi-commodity energy sources, and as a result, the chance of failures in systems and controls will increase. Therefore, systems that diagnose energy performance are of paramount importance. However, despite much research on Fault Detection and Diagnosis (FDD) methods for HVAC systems, they are rarely applied. One major reason is that proposed methods are different from the approaches taken by HVAC designers who employ process and instrumentation diagrams (P&IDs). This led to the following main research question: Which FDD architecture is suitable for HVAC systems in general to support the set up and implementation of FDD methods, including energy performance diagnosis? First, an energy performance FDD architecture based on information embedded in P&IDs was elaborated. The new FDD method, called the 4S3F method, combines systems theory with data analysis. In the 4S3F method, the detection and diagnosis phases are separated. The symptoms and faults are classified into 4 types of symptoms (deviations from balance equations, operating states (OS) and energy performance (EP), and additional information) and 3 types of faults (component, control and model faults). Second, the 4S3F method has been tested in four case studies. In the first case study, the symptom detection part was tested using historical Building Management System (BMS) data for a whole year: the combined heat and power plant of the THUAS (The Hague University of Applied Sciences) building in Delft, including an aquifer thermal energy storage (ATES) system, a heat pump, a gas boiler and hot and cold water hydronic systems. This case study showed that balance, EP and OS symptoms can be extracted from the P&ID and the presence of symptoms detected. In the second case study, a proof of principle of the fault diagnosis part of the 4S3F method was successfully performed on the same HVAC system extracting possible component and control faults from the P&ID. A Bayesian Network diagnostic, which mimics the way of diagnosis by HVAC engineers, was applied to identify the probability of all possible faults by interpreting the symptoms. The diagnostic Bayesian network (DBN) was set up in accordance with the P&ID, i.e., with the same structure. Energy savings from fault corrections were estimated to be up to 25% of the primary energy consumption, while the HVAC system was initially considered to have an excellent performance. In the third case study, a demand-driven ventilation system (DCV) was analysed. The analysis showed that the 4S3F method works also to identify faults on an air ventilation system.
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There is an urgent need for energy renovation of the existing building stock, in order to reach the climate goals, set in Paris in 2016. To reach climate targets, it is important to considerably lower energy demand as well as switch to fossil-free heating systems. Unfortunately, renovation rates across the EU remain at a low level of 1% per year. Deep renovation, which lowers energy use with 60% or more, accounts only for 0,2% of renovations. The heating transition thus progresses much more slowly than the electricity transition. We draw on the framework of technological innovation systems, which allows comparison of different transitions. In the literature, it is argued that the configurational nature of the renovation system is one of the main reasons for the slow heating transition. The renovation system is context-bound and consists of many actors both on the demand-side and the supply-side, which leads to a fragmented market. For increasing the speed of the heating transition, it is deemed important to counter this fragmentation. We carried out a review of reports and publications of EU-funded projects on energy renovation. In many projects fragmentation in the building sector was identified as one of the main obstacles. We analyzed the deliverables of these energy renovation projects to find tried and tested solutions. One of these is the so-called one-stop-shop, which promises to improve the organization of the supply side, while also providing an appropriate and affordable solution to the customer. In the discussion we argue that the energy renovation system could be improved by increasing collaboration on the supply side and at the same time simplifying the renovation process for customers. A promising tool to make this happen is the one-stop-shop.
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In this article a generic fault detection and diagnosis (FDD) method for demand controlled ventilation (DCV) systems is presented. By automated fault detection both indoor air quality (IAQ) and energy performance are strongly increased. This method is derived from a reference architecture based on a network with 3 generic types of faults (component, control and model faults) and 4 generic types of symptoms (balance, energy performance, operational state and additional symptoms). This 4S3F architecture, originally set up for energy performance diagnosis of thermal energy plants is applied on the control of IAQ by variable air volume (VAV) systems. The proposed method, using diagnosis Bayesian networks (DBNs), overcomes problems encountered in current FDD methods for VAV systems, problems which inhibits in practice their wide application. Unambiguous fault diagnosis stays difficult, most methods are very system specific, and finally, methods are implemented at a very late stage, while an implementation during the design of the HVAC system and its control is needed. The IAQ 4S3F method, which solves these problems, is demonstrated for a common VAV system with demand controlled ventilation in an office with the use of a whole year hourly historic Building Management System (BMS) data and showed it applicability successfully. Next to this, the influence of prior and conditional probabilities on the diagnosis is studied. Link to the formal publication via its DOI https://doi.org/10.1016/j.buildenv.2019.106632
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