Zoekresultaten

Blockpub Consolidated Report

Names of group members: Tran Khanh Linh Nguyen (461345) – HBO ICT TI Samad Shaikh (444629) – HBO ICT SE Jeroen Stol (453483) – HBO ICT ITSM Jelle van der Weijden (440265) – International Business Dylan Severs (468038) – International Business Jarno van Leeuwen (463788) – Business Administration Milan Kok (469346) – Business Administration Due to the increasing digitalization in the 21st century, the phenomenon to only publish scientific articles online is becoming more common. The debate on the consolidating publishing industry has been arising in the last years due to the high-profit margins that the big publishing companies are earning (Larivière, Haustein, & Mongeon, 2015). After analysing roughly 45 million papers over the period 1973 –2013, research proofs that the academic publishing industry is shifting to an oligopoly with a few publishers who publish over 50% of all published papers (Larivière, Haustein, & Mongeon, 2015). Not only do these big publishers earn revenues from the backs of writers, sometimes they also possess the copyrights of the published articles. Since the introduction of the open-access movement, it is becoming more common for publishers to request the execution of the authors ' copyright, which will be claimed by the publisher after the publication of the article (RUG, 2021). As this movement is influencing the market, more writers are considering other options. Although some writers can accept that the only important factor of publishing papers is the increasing h-index, others prefer lucrative writing and maintaining the copyrights of their publications. To stimulate that the copyrights and earnings stay with the author of the publication, our client asked us to develop BlockPub.

Controlling the Valves

Conflict lies at the core of urban sustainability transitions and the indispensable structural changes that accompany them. In this chapter we examine the RESILIO project, a multi-actor collaboration in Amsterdam aiming to transition towards a 'climate proof' city through smart water retention systems on urban roofs. The focus is on the conflict that emerged during discussions about controlling the smart valves on the rooftops which are designed to prevent urban flooding. Using a discourse analytical framework, the study analyses participant interactions, conflicting positions, and discursive strategies employed by the partners involved in the initiative. Participants utilised several discursive strategies, including identity, stake, and accountability management, to manage their positions in the conflict and influence the discourse. The study highlights the challenges of addressing conflict that involves redefining accountability and responsibility between public and private actors in the collaborative setting of transition initiatives. By doing so the findings contribute to a deeper understanding of how conflict can shape learning processes and foster sustainable urban transitions.

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.

E-mobility

Better Futures with Games and Immersive Media.

Following the Sector Protocol for Quality Assurance for Practice-Based.Contributors Academy for AI, Games and Media:Mata Haggis Burridge (prof. EG), Qiqi Zhou, Hillevi Boerboom, Maria Pafi (postdoc, WuR), Alexander van Buggenum, Ella Betts, Wilma Franchimon (dir. AGM), Nick van Apeldoorn (Coord.Digireal), Harald Warmelink (Coord. Cradle & MSP Challenge), Magali Patrocínio Gonçalves, Ard Bonewald (MT Games), Marin Hekman, Marie Lhuissier, Carlos Santos (CTO Cradle), Jeremiah van Oosten (MT, games), Kevin Hutchinson, Frank Peters (MT ADS&AI), Bram Heijligers, Joey Relouw, Marnix van Gisbergen (Prof. DMC), Shima Rezaei Rashnoodi (Coord. DMC), Phil de Groot, Igor Mayer (prof. SG), Niels Voskens, Fabio Ferreira da Costa Campos, Tuki Clavero, Jens Hagen, Wilco Boode, Natalia Harazhanka-Pietjouw (PPC), Jacopo Fabrini & Silke Hassreiter.

Measuring safety in aviation

As part of their SMS, aviation service providers are required to develop and maintain the means to verify the safety performance of their organisation and to validate the effectiveness of safety risk controls. Furthermore, service providers must verify the safety performance of their organisation with reference to the safety performance indicators and safety performance targets of the SMS in support of their organisation’s safety objectives. However, SMEs lack sufficient data to set appropriate safety alerts and targets, or to monitor their performance, and no other objective criteria currently exist to measure the safety of their operations. The Aviation Academy of the Amsterdam University of Applied Sciences therefore took the initiative to develop alternative safety performance metrics. Based on a review of the scientific literature and a survey of existing safety metrics, we proposed several alternative safety metrics. After a review by industry and academia, we developed two alternative metrics into tools to help aviation organisations verify the safety performance of their organisations.The AVAV-SMS tool measures three areas within an organisation’s Safety Management System:• Institutionalisation (design and implementation along with time and internal/external process dependencies).• Capability (the extent to which managers have the capability to implement the SMS).• Effectiveness (the extent to which the SMS deliverables add value to the daily tasks of employees).The tool is scalable to the size and complexity of the organisation, which also makes it useful for small and medium-sized enterprises (SMEs). The AVAS-SCP tool also measures three areas in the organisation’s safety culture prerequisites to foster a positive safety culture:• Organisational plans (whether the company has designed/documented each of the safety cultureprerequisites).• Implementation (the extent to which the prerequisites are realised by the managers/supervisors acrossvarious organisational levels).• Perception (the degree to which frontline employees perceive the effects of managers’ actions relatedto safety culture).We field-tested these tools, demonstrating that they have adequate sensitivity to capture gaps between Work-as-Imagined (WaI) and Work-as-Done (WaD) across organisations. Both tools are therefore useful to organisations that want to self-assess their SMS and safety culture prerequisite levels and proceed to comparisons among various functions and levels and/or over time. Our field testing and observations during the turn-around processes of a regional airline confirm that significant differences exist between WaI and WaD. Although these differences may not automatically be detrimental to safety, gaining insight into them is clearly necessary to manage safety. We conceptually developed safety metrics based on the effectiveness of risk controls. However, these could not be fully field-tested within the scope of this research project. We recommend a continuation of research in this direction. We also explored safety metrics based on the scarcity of resources and system complexity. Again, more research is required here to determine whether these provide viable solutions.