The municipality of Apeldoorn had polled the interest among its private home-owners to turn their homes energy neutral. Based on the enthusiastic response, Apeldoorn saw the launch of the Energy Apeldoorn (#ENEXAP) in 2011. Its goal was to convert to it technically and financially possible for privately owned homes to be refurbished and to energy neutral, taking the residential needs and wishes from occupants as the starting point. The project was called an Expedition, because although the goal was clear, the road to get there wasn’t. The Expedition team comprised businesses, civil-society organisations, the local university of applied sciences, the municipality of Apeldoorn, and of course, residents in a central role. The project was supported by Platform31, as part of the Dutch government’s Energy Leap programme. The #ENEXAP involved 38 homes, spread out through Apeldoorn and surrounding villages. Even though the houses were very diverse, the group of residents was quite similar: mostly middle- aged, affluent people who highly value the environment and sustainability. An important aspect of the project was the independent and active role residents played. In collaboration with businesses and professionals, through meetings, excursions, workshops and by filling in a step- by-step plan on the website, the residents gathered information about their personal situation, the energy performance of their home and the possibilities available for them to save and generate energy themselves. Businesses were encouraged to develop an integrated approach for home-owners, and consortia were set up by businesses to develop the strategy, products and services needed to meet this demand. On top of making minimal twenty from the thirty-eight houses in the project energy neutral, the ultimate goal was to boost the local demand for energy- neutral refurbishment and encourage an appropriate supply of services, opening up the (local) market for energy neutral refurbishment. This paper will reflect on the outcomes of this collective in the period 2011-2015.
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Renewable energy sources have an intermittent character that does not necessarily match energy demand. Such imbalances tend to increase system cost as they require mitigation measures and this is undesirable when available resources should be focused on increasing renewable energy supply. Matching supply and demand should therefore be inherent to early stages of system design, to avoid mismatch costs to the greatest extent possible and we need guidelines for that. This paper delivers such guidelines by exploring design of hybrid wind and solar energy and unusual large solar installation angles. The hybrid wind and solar energy supply and energy demand is studied with an analytical analysis of average monthly energy yields in The Netherlands, Spain and Britain, capacity factor statistics and a dynamic energy supply simulation. The analytical focus in this paper differs from that found in literature, where analyses entirely rely on simulations. Additionally, the seasonal energy yield profile of solar energy at large installation angles is studied with the web application PVGIS and an hourly simulation of the energy yield, based on the Perez model. In Europe, the energy yield of solar PV peaks during the summer months and the energy yield of wind turbines is highest during the winter months. As a consequence, three basic hybrid supply profiles, based on three different mix ratios of wind to solar PV, can be differentiated: a heating profile with high monthly energy yield during the winter months, a flat or baseload profile and a cooling profile with high monthly energy yield during the summer months. It is shown that the baseload profile in The Netherlands is achieved at a ratio of wind to solar energy yield and power of respectively Ew/Es = 1.7 and Pw/Ps = 0.6. The baseload ratio for Spain and Britain is comparable because of similar seasonal weather patterns, so that this baseload ratio is likely comparable for other European countries too. In addition to the seasonal benefits, the hybrid mix is also ideal for the short-term as wind and solar PV adds up to a total that has fewer energy supply flaws and peaks than with each energy source individually and it is shown that they are seldom (3%) both at rated power. This allows them to share one cable, allowing “cable pooling”, with curtailment to -for example-manage cable capacity. A dynamic simulation with the baseload mix supply and a flat demand reveals that a 100% and 75% yearly energy match cause a curtailment loss of respectively 6% and 1%. Curtailment losses of the baseload mix are thereby shown to be small. Tuning of the energy supply of solar panels separately is also possible. Compared to standard 40◦ slope in The Netherlands, facade panels have smaller yield during the summer months, but almost equal yield during the rest of the year, so that the total yield adds up to 72% of standard 40◦ slope panels. Additionally, an hourly energy yield simulation reveals that: façade (90◦) and 60◦ slope panels with an inverter rated at respectively 50% and 65% Wp, produce 95% of the maximum energy yield at that slope. The flatter seasonal yield profile of “large slope panels” together with decreased peak power fits Dutch demand and grid capacity more effectively.
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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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Energy transition is key to achieving a sustainable future. In this transition, an often neglected pillar is raising awareness and educating youth on the benefits, complexities, and urgency of renewable energy supply and energy efficiency. The Master Energy for Society, and particularly the course “Society in Transition”, aims at providing a first overview on the urgency and complexities of the energy transition. However, educating on the energy transition brings challenges: it is a complex topic to understand for students, especially when they have diverse backgrounds. In the last years we have seen a growing interest in the use of gamification approaches in higher institutions. While most practices have been related to digital gaming approaches, there is a new trend: escape rooms. The intended output and proposed innovation is therefore the development and application of an escape room on energy transition to increase knowledge and raise motivation among our students by addressing both hard and soft skills in an innovative and original way. This project is interdisciplinary, multi-disciplinary and transdisciplinary due to the complexity of the topic; it consists of three different stages, including evaluation, and requires the involvement of students and colleagues from the master program. We are confident that this proposed innovation can lead to an improvement, based on relevant literature and previous experiences in other institutions, and has the potential to be successfully implemented in other higher education institutions in The Netherlands.
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.
Within the framework of resource efficiency it is important to recycle and reusematerials, replace fossil fuel based products with bio-based alternatives and avoidthe use of toxic substances. New applications are being sought for locally grownbiomass. In the area of Groningen buildings need reinforcement to guarantee safetyfor its users, due to man-induced earthquakes. Plans are to combine the workneeded for reinforcement with the improvement of energy performance of thesebuildings. The idea is to use bio-based building materials, preferably grown andprocessed in the region.In this study it is investigated whether it is feasible to use Typha (a swap plant) as abasis for a bio-based insulation product. In order to start the activities necessary tofurther develop this idea into a commercial product and start a dedicated company,a number of important questions have to be answered in terms of feasibility. Thisstudy therefore aims at mapping economic, organisational and technical issues andassociated risks and possibilities. On the basis of these results a developmenttrajectory can be started to set up a dedicated supply chain with the appropriatepartners, research projects can be designed to develop the missing knowledge andthe required funding can be acquired.
Lectoraat, onderdeel van NHL Stenden Hogeschool
