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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Key takeaways from the project underscore the importance of fostering long-term collaborations between technical experts, communities, and institutional partners. By integrating technical innovation with human-centred design, the SUSTENANCE project has not only advanced renewable energy adoption but also established a framework for empowering communities to actively participate in sustainable energy transitions. Moving forward, the lessons learned, and solutions developed provide a solid foundation for addressing future challenges in energy system decarbonization and resilience.
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One of the mission-driven innovation policies of the Netherlands is energy transition which sets, among others, the challenge for a carbon-neutral built environment in 2050. Around 41% of Dutch houses do not yet have a registered energy label, and approximately 31% of the registered houses have label C or lower. This calls for action within the housing renovation industry. Bound to the 70 percent rule, a renovation plan requires full (or at least 70 percent) agreement on the renovation between relevant parties, including residents. In practice, agreement indicators focus mostly on economic and energy aspects. When indicators include people’s needs and preferences, it is expected to speed participation and agreement, increasing residents’ satisfaction and enhances the trust in public institutions. Tsavo was founded in 2015 to organise the sustainability of buildings for ambitious clients. Its sustainability process aims to accelerate renovation by keeping at their core value the social needs and preferences of residents. In this project Tsavo and TU Delft work together to optimise the sustainability process so, it includes everyone’s input and results in a sustainability plan that represents everyone. Tsavo’s role will be key in keeping the balance between both a sustainable renovation service that is cheaper and fast yet also attractive and with an impact on the quality of living. In this project, Tsavo’s sustainable renovation projects will be used to implement methods that focus on increasing participation and residents’ satisfaction. TU Delft will explore principles of attractive, accessible and representative activities to stimulate residents to decide on a renovation plan that is essential and meaningful to all.
In our increasingly global society, organizations face many opportunities in innovation, improved productivity and easy access to talent. At the same time, one of the greatest challenges, businesses experience nowadays, is the importance of social and/or human capital for their effectiveness and success (Backhaus and Tikoo, 2004; Mosley, 2007; Theurer et al., 2018; Tumasjan et al., 2020). High-quality employees are crucial to the competitive strength of an organization in the global economy, as these employees have a major influence on organizational reputation (Dowling at al., 2012). An important question is how, under these global circumstances, organizations and companies in the Netherlands can best be stimulated to attract and preserve social capital.Several studies have suggested the scarcity of talent and the crucial importance of gaining competitive advantage with recruitment communication to find the fit between personal and fundamental organizational characteristics and values for employees (Cable and Edwards, 2004; Bhatnagar and Srivastava, 2008; ManPower Group, 2014; European Communication Monitor (ECM), 2018). In order to become an employer of choice, organizations have to not only stand out from the crowd during the recruitment process but work on developing loyalty and a culture of trust in their relationship with employees (ECM, 2018). Employer Branding focuses on the process of promoting an organization, as the “employer of choice” to a desired target group, which an organization aims to attract and retain. This process encompasses building an identifiable and unique employer identity or, more specifically, “the promotion of a unique and attractive image” as an employer (Backhaus 2004, p. 117; Backhaus and Tikoo 2004, p. 502).One of the biggest challenges in the North of the Netherlands at the moment is the urgent need for qualified labor in the IT, energy and healthcare sectors and the excess supply of international graduates who are able to find a job in the North of the Netherlands (AWVN, 2019). Talent development, as part of the regional labor market and education policy, has been an important part of government programs and strategies in the region (VNO-NCW Noord, 2018). For instance, North Netherlands Alliance (SNN) signed a Northern Innovation Agenda for the 2014-2020 period. SNN encourages, facilitates and connects ambitions focused on the development of the Northern Netherlands. Also, the Social Economic council North Netherlands issued an advice on the labour market in the North Netherlands (SER Noord Nederland, 2017). Knowledge institutions also contribute through employability programs. Another example is the Regional Talent Agreement (Talent Akkoord) framework issued by the Groningen educational institutions, employers and employees’ organizations and regional authorities in which they jointly commit to recruiting, training, retaining and developing talent for the Northern labor market. Most of the hires with a maximum of five year of experience at companies are represented by millennials. To learn what values make an attractive brand for employees in the of the North of the Netherlands, we conducted a first study. When ranking the most important values of corporate culture which matter to young employees, they mention creative freedom, purposeful work, flexibility, work-life balance as well as personal development. Whereas attractive workplace and job security do not matter to such a degree. A positive work environment and a good relationship with colleagues are valued highly (Hein, 2019).To date, as far as we know, no other employer branding studies have been carried out for the North of the Netherlands. Further insight is needed into the role of employer branding as a powerful tool to retain talent in Northern industry in particular.The goal of this study is to provide a detailed analysis of the regional industry in the Northern Netherlands and contribute to: 1) the scientific body of knowledge about whether and how employer branding can strengthen the attractiveness of a regional industry in the labor market; 2) the application of this knowledge and insights by companies and governments in local policy development in the North of the Netherlands.
The SPRONG group, originating from the CoE KennisDC Logistiek, focuses on 'Low Impact in Lastmile Logistics' (LILS). The LILS group conducts practical research with local living labs and learning communities. There is potential for more collaboration and synergy for nationwide scaling of innovations, which is currently underutilized. LILS aims to make urban logistics more sustainable and facilitate necessary societal transitions. This involves expanding the monodisciplinary and regional scope of CoE KennisDC Logistiek to a multidisciplinary and supra-regional approach, incorporating expertise in spatial planning, mobility, data, circularity, AI, behavior, and energy. The research themes are:- Solutions in scarce space aiming for zero impact;- Influencing behavior of purchasers, recipients, and consumers;- Opportunities through digitalization.LILS seeks to increase its impact through research and education beyond its regions. Collaboration between BUas, HAN, HR, and HvA creates more critical mass. LILS activities are structured around four pillars:- Developing a joint research and innovation program in a roadmap;- Further integrating various knowledge domains on the research themes;- Deepening methodological approaches, enhancing collaboration between universities and partners in projects, and innovating education (LILS knowledge hub);- Establishing an organizational excellence program to improve research professionalism and quality.These pillars form the basis for initiating and executing challenging, externally funded multidisciplinary research projects. LILS is well-positioned in regions where innovations are implemented and has a strong national and international network and proven research experience.Societal issue:Last-mile logistics is crucial due to its visibility, small deliveries, high costs, and significant impact on emissions, traffic safety, and labor hours. Lastmile activities are predicted to grow a 20% growth in the next decade. Key drivers for change include climate agreements and energy transitions, urban planning focusing on livability, and evolving retail landscapes and consumer behavior. Solutions involve integrating logistics with spatial planning, influencing purchasing behavior, and leveraging digitalization for better data integration and communication. Digital twins and the Physical Internet concept can enhance efficiency through open systems, data sharing, asset sharing, standardization, collaboration protocols, and modular load units.Key partners: Buas, HR, HAN, HvAPartners: TNO, TU Delft, Gemeente Rotterdam, Hoger Onderwijs Drechtsteden, Significance, Metropolitan Hub System, evofenedex, Provincie Gelderland, Duurzaam Bereikbaar Heijendaal, Gemeente Alphen aan den Rijn, Radboud Universiteit, I&W - DMI, DHL, TLN, Noorderpoort, Fabrications, VUB, Smartwayz, RUG, Groene Metropoolregio.
