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Escaperoom in Energy education

This paper focuses on utilizing the Celciushouse as an escape room in energy education. In a broader context, it also addresses the incorporation of serious gaming in education. The project is part of COVE SEED. SEED - Sustainable Energy Education, aims to develop innovative vocational education and training, working with experts from five different European regions to phase out fossil fuels and contributing to Europe becoming a fossil free energy continent. SEED is a CoVE (Centres of Vocational Excellence) programme. CoVE’s are part of the Erasmus+ program aiming to establish transnational platforms on, among others, regional development, innovation and inclusion. SEED combines education on various international levels including level 2,3,4, and 6. At this moment, the project ESCAPEROOM IN ENERGY EDUCATION is still in its initial phase. With this paper and the accompanying workshop, we aim to gather insights from other international regions involved in the SEED project collaboration. The acceleration of technological developments means that what is learned today may be outdated tomorrow. Therefore, it is essential for educational institutions to focus on developing general skills such as critical thinking, problem-solving, and the ability to quickly absorb new information. The market demands professionals with modern knowledge and skills. Techniques taught to students today may become outdated tomorrow. Therefore, the ability to learn how to learn is becoming increasingly crucial. Analytical and research skills are therefore gaining importance. It is also essential for students to utilize various learning methods. Not just learning from books but particularly learning from practical experience. Practice-oriented learning, where students gain direct experience in real situations, not only reinforces theoretical knowledge but also develops practical skills that are valuable in the job market. To tackle these problems, serious gaming or the establishment of escape rooms can be a solution.

Lessons From EU-Projects for Energy Renovation

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.

Building and building systems energy prediction models to enhance energy flexibility control

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.

Simulation of free-floating vehicle charging behaviour at public charging points

As society has to adapt to changing energy sources and consumption, it is driving away from fossil energy. One particular area of interest is electrical driving and the increasing demand for (public) charging facilities. For municipalities, it is essential to adapt to this changing demand and provide more public charging facilities.In order to accommodate on roll-out strategies in metropolitan areas a data driven simulation model, SEVA1, has been developed The SEVA base model used in this paper is an Agent-Based model that incorporate past sessions to predict future charging behaviour. Most EV users are habitual users and tend to use a small subset of the available charge facilities, by that obtaining a pattern is within the range possibilities. Yet, for non-habitual users, for example, car sharing users, obtaining a pattern is much harder as the cars use a significantly higher amount of charge points.The focus of this research is to explore different model implementations to assess the potential of predicting free-floating cars from the non-habitual user population. Most important result is that we now can simulate effects of deployement of car sharing users in the system, and with that the effect on convenience for habitual users. Results show that the interaction between habitual and non habitual EV users affect the unsuccessful connection attempts based increased based on the size of the car-sharing fleet up to approximately 10 percent. From these results implications for policy makers could be drawn.

Born to be fossil

Design of wind and solar energy supply, to match energy demand

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.