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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This research presents a case study exploring the potential for demand side flexibility at a cluster of university buildings. The study investigates the potential of a collection of various electrical devices, excluding heating and cooling systems. With increasing penetration of renewable electricity sources and the phasing out of dispatchable fossil sources, matching grid generation with grid demand will become difficult using traditional grid management methods alone. Additionally, grid congestion is a pressing problem. Demand side management in buildings may contribute to a solution to these problems. Currently demand response is, however, not yet exploited at scale. In part, this is because it is unclear how this flexibility can be translated into successful business models, or whether this is possible under the current market regime. This research gives insight into the potential value of energy demand flexibility in reducing energy costs and increasing the match between electricity demand and purchased renewable electricity. An inventory is made of on-site electrical devices that offer load flexibility and the magnitude and duration of load shifting is estimated for each group of devices. A demand response simulation model is then developed that represents the complete collection of flexible devices. This model, addresses demand response as a ‘distribute candy’ problem and finds the optimal time-of-use for shiftable electricity demand whilst respecting the flexibility constraints of the electrical devices. The value of demand flexibility at the building cluster is then assessed using this simulation model, measured electricity consumption, and data regarding the availability of purchased renewables and day-ahead spot prices. This research concludes that coordinated demand response of large variety of devices at the building cluster level can improve energy matching by 0.6-1.5% and reduce spot market energy cost by 0.4-3.2%.
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The future energy system could benefit from the integration of the independent gas, heat and electricity infrastructures. In addition to an increase in exergy efficiency, such a Hybrid Energy Network (HEN) could support the increase of intermittent renewable energy sources by offering increased operational flexibility. Nowadays, the expectations on Natural Gas resources forecast an increase in the application of Liquefied Natural Gas (LNG), as a means of storage and transportation, which has a high exergy value due to the low temperature. Therefore, we analysed the integration of a decentralized LNG regasification with a CHP (Waste-to-Energy) plant, to determine whether the integration could offer additional operational flexibility for the future energy network with intermittent renewable energy sources, under optimized exergy efficient conditions. We compared the independent system with two systems integrated by means of 1) Organic Rankine Cycle and 2) Stirling Engine using the cold of the LNG, that we analysed using a simplified deterministic model based on the energy hub concept. We use the hourly measured electricity and heat demand patterns for 200 households with 35% of the households producing electricity from PV according to a typical measured solar insolation pattern in The Netherlands. We found that for both systems the decentralized LNG regasification integrated with the W2E plant affects the imbalance of the system for electricity and heat, due to the additional redundant paths to produced electricity. The integration of the systems offers additional operational flexibility depending on the means of integration and its availability to produce additional energy carriers. For our future work, we will extend the model, taking into account the variability and randomness in the different parameters, which may cause significant changes in the performance and reliability of the model.
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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.
A fast growing percentage (currently 75% ) of the EU population lives in urban areas, using 70% of available energy resources. In the global competition for talent, growth and investments, quality of city life and the attractiveness of cities as environments for learning, innovation, doing business and job creation, are now the key parameters for success. Therefore cities need to provide solutions to significantly increase their overall energy and resource efficiency through actions addressing the building stock, energy systems, mobility, and air quality.The European Energy Union of 2015 aims to ensure secure, affordable and climate-friendly energy for EU citizens and businesses among others, by bringing new technologies and renewed infrastructure to cut household bills, create jobs and boost growth, for achieving a sustainable, low carbon and environmentally friendly economy, putting Europe at the forefront of renewable energy production and winning the fight against global warming.However, the retail market is not functioning properly. Many household consumers have too little choices of energy suppliers and too little control over their energy costs. An unacceptably high percentage of European households cannot afford to pay their energy bills. Energy infrastructure is ageing and is not adjusted to the increased production from renewables. As a consequence there is still a need to attract investments, with the current market design and national policies not setting the right incentives and providing insufficient predictability for potential investors. With an increasing share of renewable energy sources in the coming decades, the generation of electricity/energy will change drastically from present-day centralized production by gigawatt fossil-fueled plants towards decentralized generation, in cities mostly by local household and district level RES (e.g PV, wind turbines) systems operating in the level of micro-grids. With the intermittent nature of renewable energy, grid stress is a challenge. Therefore there is a need for more flexibility in the energy system. Technology can be of great help in linking resource efficiency and flexibility in energy supply and demand with innovative, inclusive and more efficient services for citizens and businesses. To realize the European targets for further growth of renewable energy in the energy market, and to exploit both on a European and global level the expected technological opportunities in a sustainable manner, city planners, administrators, universities, entrepreneurs, citizens, and all other relevant stakeholders, need to work together and be the key moving wheel of future EU cities development.Our SolutionIn the light of such a transiting environment, the need for strategies that help cities to smartly integrate technological solutions becomes more and more apparent. Given this condition and the fact that cities can act as large-scale demonstrators of integrated solutions, and want to contribute to the socially inclusive energy and mobility transition, IRIS offers an excellent opportunity to demonstrate and replicate the cities’ great potential. For more information see the HKU Smart Citieswebsite or check out the EU-website.
In this project I will examine models of local energy production and management, comparing the nine Zero emission neighbourhoods, as developed and monitored by the ZEN research centre at NTNU with the Friesian towns that are part of a new research project in the Netherlands (see above). Secondly, connected to this empirical part, I aim to examine and compare the policies and opportunities for prosumers and small and/ or cooperative businesses in Norway and The Netherlands. In this project I will examine models of local energy production and management, comparing the nine Zero emission neighbourhoods, as developed and monitored by the ZEN research centre at NTNU with the Friesian towns that are part of a new research project in the Netherlands (see above). Secondly, connected to this empirical part, I aim to examine and compare the policies and opportunities for prosumers and small and/ or cooperative businesses in Norway and The Netherlands.
