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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Background• We need to transition to sustainable energy because of several reasons (e.g., climate change)• The transition process is hampered due to lack of viable business models
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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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We have developed an SI-traceable narrow-band tunable radiance source based on an optical parametric oscillator (OPO) and an integrating sphere for the calibration of spectroradiometers. The source is calibrated with a reference detector over the ultraviolet/visible spectral range with an uncertainty of <1%. As a case study, a CubeSat spectroradiometer has been calibrated for radiance over its operating range from 370 nm to 480 nm. To validate the results, the instrument has also been calibrated with a traditional setup based on a diffuser and an FEL lamp. Both routes show good agreement within the combined measurement uncertainty. The OPO-based approach could be an interesting alternative to the traditional method, not only because of reduced measurement uncertainty, but also because it directly allows for wavelength calibration and characterization of the instrumental spectral response function and stray light effects, which could reduce calibration time and cost.
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We need mental and physical reference points. We need physical reference points such as signposts to show us which way to go, for example to the airport or the hospital, and we need reference points to show us where we are. Why? If you don’t know where you are, it’s quite a difficult job to find your way, thus landmarks and “lieux de memoire” play an important role in our lives.
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An important consideration for future age-friendly cities is that older people are able to live in housing appropriate for their needs. While thermal comfort in the home is vital for the health and well-being of older people, there are currently few guidelines about how to achieve this. This study is part of a research project that aims to improve the thermal environment of housing for older Australians by investigating the thermal comfort of older people living independently in South Australia and developing thermal comfort guidelines for people ageing-in-place. This paper describes the approach fundamental for developing the guidelines, using data from the study participants’ and the concept of personas to develop a number of discrete “thermal personalities”. Hierarchical Cluster Analysis (HCA) was implemented to analyse the features of research participants, resulting in six distinct clusters. Quantitative and qualitative data from earlier stages of the project were then used to develop the thermal personalities of each cluster. The thermal personalities represent di erent approaches to achieving thermal comfort, taking into account a wide range of factors including personal characteristics, ideas, beliefs and knowledge, house type, and location. Basing the guidelines on thermal personalities highlights the heterogeneity of older people and the context-dependent nature of thermal comfort in the home and will make the guidelines more user-friendly and useful. Original publication at MDPI: https://doi.org/10.3390/ijerph17228402 © 2020 by the authors. Licensee MDPI.
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This chapter explains in brief what is needed to achieve more sustainable manufacturing processes. It develops both aspects of sustainable, economic, and technical feasibility with most focus on the latter. Remanufacturing processes are described together with relevant factors that enhance their effectivity and efficiency. An overview is given of what kind of shopfloor innovations are required in the near future and some suggestions on how digital and other Industry 4.0 technologies could help to move toward circular manufacturing.
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This paper outlines the main differences between ecocentric and anthropocentric positions in regard to justice, exploring university students’ perceptions of the concepts of social and ecological justice and reflecting on how values assigned to humans and the environment are balanced and contested. Putting justice for people before the environment is based on evidence that biological conservation can disadvantage local communities; the idea that the very notion of justice is framed by humans and therefore remains a human issue; and the assumption that humans have a higher value than other species. Putting justice for the environment first assumes that only an ecocentric ethic guarantees protection of all species, including humans, and therefore ecological justice already guarantees social justice. This research shows that many students emphasize the convergence of social and ecological justice where human and environmental interests correspond. While not wishing to diminish the underlying assumptions of either ethical orientation, the common “enemy” of both vulnerable communities and nonhuman nature, as identified by students, is an ideology of economic growth and industrial development. http://dx.doi.org/10.13135/2384-8677/2688 LinkedIn: https://www.linkedin.com/in/helenkopnina/
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Dealing with and maintaining high-quality standards in the design and construction phases is challenging, especially for on-site construction. Issues like improper implementation of building components and poor communication can widen the gap between design specifications and actual conditions. To prevent this, particularly for energy-efficient buildings, it is vital to develop resilient, sustainable strategies. These should optimize resource use, minimize environmental impact, and enhance livability, contributing to carbon neutrality by 2050 and climate change mitigation. Traditional post-occupancy evaluations, which identify defects after construction, are impractical for addressing energy performance gaps. A new, real-time inspection approach is necessary throughout the construction process. This paper suggests an innovative guideline for prefabricated buildings, emphasizing digital ‘self-instruction’ and ‘self-inspection’. These procedures ensure activities impacting quality adhere to specific instructions, drawings, and 3D models, incorporating the relevant acceptance criteria to verify completion. This methodology, promoting alignment with planned energy-efficient features, is supported by BIM-based software and Augmented Reality (AR) tools, embodying Industry 4.0 principles. BIM (Building Information Modeling) and AR bridge the gap between virtual design and actual construction, improving stakeholder communication and enabling real-time monitoring and adjustments. This integration fosters accuracy and efficiency, which are key for energy-efficient and nearly zero-energy buildings, marking a shift towards a more precise, collaborative, and environmentally sensible construction industry.
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With a market demand for low cost, easy to produce, flexible and portable applications in healthcare, energy, biomedical or electronics markets, large research programs are initiated to develop new technologies to provide this demand with new innovative ideas. One of these fast developing technologies is organic printed electronics. As the term printed electronics implies, functional materials are printed via, e.g. inkjet, flexo or gravure printing techniques, on to a substrate material. Applications are, among others, organic light emitting diodes (OLED), sensors and Lab-on-a-chip devices. For all these applications, in some way, the interaction of fluids with the substrate is of great importance. The most used substrate materials for these low-cost devices are (coated) paper or plastic. Plastic substrates have a relatively low surface energy which frequently leads to poor wetting and/or poor adhesion of the fluids on the substrates during printing and/ or post-processing. Plasma technology has had a long history in treating materials in order to improve wetting or promote adhesion. The µPlasma patterning tool described in this thesis combines a digital inkjet printing platform with an atmospheric dielectric barrier discharge plasma tool. Thus enabling selective and local plasma treatment, at atmospheric pressure, of substrates without the use of any masking materials. In this thesis, we show that dependent on the gas composition the substrate surface can either be functionalized, thus increasing its surface energy, or material can be deposited on the surface, lowering its surface energy. Through XPS and ATR-FTIR analysis of the treated (polymer) substrate surfaces, chemical modification of the surface structure was confirmed. The chemical modification and wetting properties of the treated substrates remained present for at least one month after storage. Localized changes in wettability through µPlasma patterning were obtained with a resolution of 300µm. Next to the control of wettability of an ink on a substrate in printed electronics is the interaction of ink droplets with themselves of importance. In printing applications, coalescence of droplets is standard practice as consecutive droplets are printed onto, or close to each other. Understanding the behaviour of these droplets upon coalescence is therefore important, especially when the ink droplets are of different composition and/or volume. For droplets of equal volume, it was found that dye transport across the coalescence bridge could be fully described by diffusion only. This is as expected, as due to the droplet symmetry on either side of the bridge, the convective flows towards the bridge are of equal size but opposite in direction. For droplets of unequal volume, the symmetry across the bridge is no longer present. Experimental analysis of these merging droplets show that in the early stages of coalescence a convective flow from the small to large droplet is present. Also, a smaller convective flow of shorter duration from the large into the small droplet was identified. The origin of this flow might be due to the presence of vortices along the interface of the bridge, due to the strong transverse flow to open the bridge. To conclude, three potential applications were showcased. In the first application we used µPlasma patterning to create hydrophilic patterns on hydrophobic dodecyl-trichlorosilane (DTS) covered glass. Capillaries for a Lab-on-a-chip device were successfully created by placing two µPlasma patterned glass slides on top of each other separated by scotch tape. In the second application we showcased the production of a RFID tag via inkjet printing. Functional RFID-tags on paper were created via inkjet printing of silver nanoparticle ink connected to an integrated circuit. The optimal operating frequency of the produced tags is in the range of 860-865 MHz, making them usable for the European market, although the small working range of 1 m needs further improvement. Lastly, we showed the production of a chemresistor based gas sensor. In house synthesised polyemeraldine salt (PANi) was coated by hand on top of inkjet printed silver electrodes. The sensor proved to be equally sensitive to ethanol and water vapour, reducing its selectivity in detecting changes in gas composition.
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