The pace of introduction of new technology and thus continuous change in skill needs at workplaces, especially for the engineers, has increased. While digitization induced changes in manufacturing, construction and supply chain sectors may not be felt the same in every sector, this will be hard to escape. Both young and experienced engineers will experience the change, and the need to continuously assess and close the skills gap will arise. How will we, the continuing engineering educators and administrators will respond to it? Prepared for engineering educators and administrators, this workshop will shed light on the future of continuing engineering education as we go through exponentially shortened time frames of technological revolution and in very recent time, in an unprecedented COVID-19 pandemic. S. Chakrabarti, P. Caratozzolo, E. Sjoer and B. Norgaard.
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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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This article provides a nano (hyperlocal) view of climate change mitigation by viewing regenerative organizing through the eyes (as well as bodies and senses, etc.) of the households engaged in community-based energy projects. By showing what humans make up for in the largely absent relationship between nature and technology in these projects, we envision an incremental extension of the literature on community-based energy. The radically different contribution we aim to make is a tripartite imbrication that brings in natural agency alongside the human and the technical but specifies precisely how nano (smaller than micro) embodied practices afford mis- and realignments. https://doi.org/10.1177/1086026619886841 LinkedIn: https://www.linkedin.com/in/helenkopnina/
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Electrification of transportation, communication, working and living continues worldwide. Televisions, telephones, servers are an important part of everyday life. These loads and most sustainable sources as well, have one thing in common: Direct Current. The Dutch research and educational programme ‘DC – road to its full potential’ studies the impact of feeding these appliances from a DC grid. An improvement in energy efficiency is expected, other benefits are unknown and practical considerations are needed to come to a proper comparison with an AC grid. This paper starts with a brief introduction of the programme and its first stages. These stages encompass firstly the commissioning, selection and implementation of a safe and user friendly testing facility, to compare performance of domestic appliances when powered with AC and DC. Secondly, the relationship between the DC-testing facility and existing modeling and simulation assignments is explained. Thirdly, first results are discussed in a broad sense. An improved energy efficiency of 3% to 5% is already demonstrated for domestic appliances. That opens up questions for the performance of a domestic DC system as a whole. The paper then ends with proposed minor changes in the programme and guidelines for future projects. These changes encompass further studying of domestic appliances for product-development purposes, leaving less means for new and costly high-power testing facilities. Possible gains are 1) material and component savings 2) simpler and cheaper exteriors 3) stable and safe in-house infrastructure 4) whilst combined with local sustainable generation. That is the road ahead. 10.1109/DUE.2014.6827758
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This paper describes a model for education in innovative engineering. The kernel of this model is, that students from different departments of the faculty of Applied Science and Technology are placed in industry for a period of eighteen months after two-and-a-half year of theoretical studies. During this period students work in multi-disciplinary projects on different themes. Students will grow to fully equal employees in industry. Therefore it is important that besides students, teachers and company employees will participate in the projects. Also the involvement of other level students (University and high school) is recommended. The most important characteristics of the model can be summarized in innovative, interdisciplinary and international orientation.
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An important performance determinant in wheelchair sports is the power exchanged between the athletewheelchair combination and the environment, in short, mechanical power. Inertial measurement units (IMUs) might be used to estimate the exchanged mechanical power during wheelchair sports practice. However, to validly apply IMUs for mechanical power assessment in wheelchair sports, a well-founded and unambiguous theoretical framework is required that follows the dynamics of manual wheelchair propulsion. Therefore, this research has two goals. First, to present a theoretical framework that supports the use of IMUs to estimate power output via power balance equations. Second, to demonstrate the use of the IMU-based power estimates during wheelchair propulsion based on experimental data. Mechanical power during straight-line wheelchair propulsion on a treadmill was estimated using a wheel mounted IMU and was subsequently compared to optical motion capture data serving as a reference. IMU-based power was calculated from rolling resistance (estimated from drag tests) and change in kinetic energy (estimated using wheelchair velocity and wheelchair acceleration). The results reveal no significant difference between reference power values and the proposed IMU-based power (1.8% mean difference, N.S.). As the estimated rolling resistance shows a 0.9–1.7% underestimation, over time, IMU-based power will be slightly underestimated as well. To conclude, the theoretical framework and the resulting IMU model seems to provide acceptable estimates of mechanical power during straight-line wheelchair propulsion in wheelchair (sports) practice, and it is an important first step towards feasible power estimations in all wheelchair sports situations.
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Demand Conference Lancaster 2016Workshop 9. Space, site and scale in the making of energy demandAbstract: Energy scripts and spacesTineke van der Schoor, Hanze University of Applied Sciences, GroningenTechnology is infused with scripts that indicate how we as users should behave around, live in or use an artefact. Drawing inspiration from literature discussing user scripts and gender scripts, we develop the notion of energy scripts. We tentatively define the concept of energy scripts as the way the distribution of light, heat and power within a building choreographs its functional use and stimulates or discourages energy use. We apply this concept to buildings, to analyse if and how the energy demand of buildings is choreographed by architectural design.In the literature, scripts are also investigated in a normative setting. Designers, such as engineers or architects, embed their worldviews into artefacts thus providing an opportunity where scripts ‘materialize morality’. However, users are not necessarily the passive receptacles of these embedded scripts; they have opportunities to ignore, resist or even redesign built artefacts.In European architectural design, it can be seen in the middle ages that the situation of rooms was such that important functions –such as writing manuscripts - could profit from heat produced in the kitchen. Moreover, the distribution of warmth followed the division of labour and class lines, as servant’s quarters were unheated. Modern houses also have energy-scripts, for example kitchens are designed for housing separate appliances, instead of using cool storage. The use of technology for heating and lighting is ubiquitous in modern buildings, while the need to reduce energy demand often leads to the installation of even more technology, smart or otherwise. On the other hand, ‘passive design’ demonstrates that it isquite possible to design buildings that need almost no energy for heating. Furthermore, new ‘daylighting’ technologies bring natural light to the darkest spaces. Historically, there have been ‘paths not taken’, which could have led to a less energy demanding built environment. Retracing these paths can lead to new perspectives on building design and retrofit.Researching the concept of energy-scripts we contribute to our understanding of the constraints and flexibilities for reduced energy demand in buildings. Our approach also sheds light on the social construction of the ‘resident’ or ’house consumer’ as an end-user. Investigating implicit expectations regarding energy use, which could ultimately assist in designing building scripts that specifically invite energy efficient use of a building.
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This paper is a case report of why and how CDIO became a shared framework for Community Service Engineering (CSE) education. CSE can be defined as the engineering of products, product-service combinations or services that fulfill well-being and health needs in the social domain, specifically for vulnerable groups in society. The vulnerable groups in society are growing, while fewer people work in health care. Finding technical, interdisciplinary solutions for their unmet needs is the territory of the Community Service Engineer. These unmet needs arise in local niche markets as well as in the global community, which makes it an interesting area for innovation and collaboration in an international setting. Therefore, five universities from Belgium, Portugal, the Netherlands, and Sweden decided to work together as hubs in local innovation networks to create international innovation power. The aim of the project is to develop education on undergraduate, graduate and post-graduate levels. The partners are not aiming at a joined degree or diploma, but offer a shared short track blended course (3EC), which each partner can supplement with their own courses or projects (up to 30EC). The blended curriculum in CSE is based on design thinking principles. Resources are shared and collaboration between students and staff is organized at different levels. CDIO was chosen as the common framework and the syllabus 2.0 was used as a blueprint for the CSE learning goals in each university. CSE projects are characterized by an interdisciplinary, human centered approach leading to inter-faculty collaboration. At the university of Porto, EUR-ACE was already used as the engineering education framework, so a translation table was used to facilitate common development. Even though Thomas More and KU Leuven are no CDIO partner, their choice for design thinking as the leading method in the post-Masters pilot course insured a good fit with the CDIO syllabus. At this point University West is applying for CDIO and they are yet to discover what the adaptation means for their programs and their emerging CSE initiatives. CDIO proved to fit well to in the authentic open innovation network context in which engineering students actively do CSE projects. CDIO became the common language and means to continuously improve the quality of the CSE curriculum.
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Peer-to-peer (P2P) energy trading has been recognized as an important technology to increase the local self-consumption of photovoltaics in the local energy system. Different auction mechanisms and bidding strategies haven been investigated in previous studies. However, there has been no comparatively analysis on how different market structures influence the local energy system’s overall performance. This paper presents and compares two market structures, namely a centralized market and a decentralized market. Two pricing mechanisms in the centralized market and two bidding strategies in the decentralized market are developed. The results show that the centralized market leads to higher overall system self-consumption and profits. In the decentralized market, some electricity is directly sold to the grid due to unmatchable bids and asks. Bidding strategies based on the learning algorithm can achieve better performance compared to the random method.
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