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.
Uit het rapport: "Deze onderzoeksagenda is tot stand gebracht door de lectoren die samenwerken in het Nationaal Lectoren Platform Urban Energy. Alle betrokkenen bij het platform zijn in staat gesteld om bij te dragen aan de tekst, speciale dank daarbij voor de bijdragen en commentaren vanuit de TKI Urban Energy en de HCA topsector Energie."
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.
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.
Verschillende maatschappelijke veranderingen dwingen de bouwbranche tot innovaties. Ondanks de potentie op het vlak van circulariteit en duurzaamheid van 3D-printen met kunststoffen kent deze technologie nog nauwelijks toepassingen in de bouw. Redenen hiervoor zijn achterblijvende materiaaleigenschappen en het verschil in cultuur tussen de bouwwereld en kunststofverwerkende industrie. Het bedrijf Phidias, richt zich op innovatieve en creatieve vastgoedconcepten. Samen met Zuyd Hogeschool (Zuyd) willen zij onderzoek doen naar het printen van bouwelementen waarbij de meerwaarde van 3D-printen wordt gezien in het combineren van materiaaleigenschappen. Zuyd heeft afgelopen jaren veel onderzoek gedaan naar het ontwikkelen van materialen voor 3D-printen (o.a. 2014-01-96 PRO). De volgende fase is de opgedane kennis toe te passen voor specifieke applicaties, in dit geval om de vraag van het MKB bedrijf Phidias te beantwoorden. Vanuit een ander MKB-bedrijf, MaukCC, ontwikkelaar van 3D printers, komt de vraag om de afstemming tussen materialen en hardware te optimaliseren. De combinatie van beide vragen uit het werkveld en de expertise bij Zuyd heeft geleid tot dit projectvoorstel. In deze pilotstudie ligt de focus voornamelijk op het 3D printen van één specifiek bouwkundig element met meerdere eigenschappen (bouwfysisch en constructief). De combinatie van eigenschappen wordt verkregen door gebruik te maken van twee (biobased) kunststoffen waarbij tevens een variatie wordt aangebracht in de geprinte structuren. Op deze manier kunnen grondstoffen worden gespaard. Het onderzoek sluit aan bij twee zwaartepunten van Zuyd, namelijk “Transitie naar een duurzaam gebouwde omgeving” en “Life science & materials”. De interdisciplinaire aanpak, op het grensvlak van de lectoraten “Material Sciences” (Gino van Strydonck) en “Sustainable Energy in the Built Environment” (Zeger Vroon) staat garant voor innovatief onderzoek. Integratie van onderwijs en onderzoek vindt plaats door studenten samen met een coach (docent) en ervaren professional aan dit onderzoek te laten werken in Communities for Development (CfD’s).
The denim industry faces many complex sustainability challenges and has been especially criticized for its polluting and hazardous production practices. Reducing resource use of water, chemicals and energy and changing denim production practices calls for collaboration between various stakeholders, including competing denim brands. There is great benefit in combining denim brands’ resources and knowledge so that commonly defined standards and benchmarks are developed and realized on a scale that matters. Collaboration however, and especially between competitors, is highly complex and prone to fail. This project brings leading denim brands together to collectively take initial steps towards improving the ecological sustainability impact of denim production, particularly by establishing measurements, benchmarks and standards for resource use (e.g. chemicals, water, energy) and creating best practices for effective collaboration. The central research question of our project is: How do denim brands effectively collaborate together to create common, industry standards on resource use and benchmarks for improved ecological sustainability in denim production? To answer this question, we will use a mixed-method, action research approach. The project’s research setting is the Amsterdam Metropolitan Area (MRA), which has a strong denim cluster and is home to many international denim brands and start-ups.
