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.
The EU Maritime Spatial Planning Directive (MSPD) requires the member states (MS) to pursue Blue Growth while ensuring good environmental status (GES) of sea areas. An ecosystem-based approach (EBA) should be used for the integration of the aims. However, the MSPD does not specify how the MS should arrange their MSP governance, which has led to a variety of governance arrangements and solutions in addressing the aims. We analysed the implementation of the MSPD in Finland, to identify conditions that may enable or constrain the integration of Blue Growth and GES in the framework of EBA. MSP in Finland is an expert-driven regionalized approach with a legally non-binding status. The results suggest that this MSP framework supports the implementation of EBA in MSP. Yet, unpredictability induced by the non-binding status of MSP, ambiguity of the aims of MSP and of the concept of EBA, and the need to pursue economic viability in the coastal municipalities may threaten the consistency of MSP in both spatial and temporal terms. Developing MSP towards a future-oriented adaptive and collaborative approach striving for social learning could improve the legitimacy of MSP and its capacity to combine Blue Growth and GES. The analysis indicates, that in the delivery of successful MSP adhering to the principles of EBA should permeate all levels of governance. The study turns attention to the legal status of MSP as a binding or non-binding planning instrument and the role the legal status plays in facilitating or constraining predictability and adaptability required in MSP.
MULTIFILE
Data-driven condition-based maintenance (CBM) and predictive maintenance (PdM) strategies have emerged over recent years and aim at minimizing the aviation maintenance costs and environmental impact by the diagnosis and prognosis of aircraft systems. As the use of data and relevant algorithms is essential to AI-based gas turbine diagnostics, there are different technical, operational, and regulatory challenges that need to be tackled in order for the aeronautical industry to be able to exploit their full potential. In this work, the machine learning (ML) method of the generalised additive model (GAM) is used in order to predict the evolution of an aero engine’s exhaust gas temperature (EGT). Three different continuous synthetic data sets developed by NASA are employed, known as New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS), with increasing complexity in engine deterioration. The results show that the GAM can be predict the evolution of the EGT with high accuracy when using several input features that resemble the types of physical sensors installed in aero gas turbines currently in operation. As the GAM offers good interpretability, this case study is used to discuss the different data attributes a data set needs to have in order to build trust and move towards certifiable models in the future.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
A fast growing percentage (currently 75% ) of the EU population lives in urban areas, using 70% of available energy resources. In the global competition for talent, growth and investments, quality of city life and the attractiveness of cities as environments for learning, innovation, doing business and job creation, are now the key parameters for success. Therefore cities need to provide solutions to significantly increase their overall energy and resource efficiency through actions addressing the building stock, energy systems, mobility, and air quality.The European Energy Union of 2015 aims to ensure secure, affordable and climate-friendly energy for EU citizens and businesses among others, by bringing new technologies and renewed infrastructure to cut household bills, create jobs and boost growth, for achieving a sustainable, low carbon and environmentally friendly economy, putting Europe at the forefront of renewable energy production and winning the fight against global warming.However, the retail market is not functioning properly. Many household consumers have too little choices of energy suppliers and too little control over their energy costs. An unacceptably high percentage of European households cannot afford to pay their energy bills. Energy infrastructure is ageing and is not adjusted to the increased production from renewables. As a consequence there is still a need to attract investments, with the current market design and national policies not setting the right incentives and providing insufficient predictability for potential investors. With an increasing share of renewable energy sources in the coming decades, the generation of electricity/energy will change drastically from present-day centralized production by gigawatt fossil-fueled plants towards decentralized generation, in cities mostly by local household and district level RES (e.g PV, wind turbines) systems operating in the level of micro-grids. With the intermittent nature of renewable energy, grid stress is a challenge. Therefore there is a need for more flexibility in the energy system. Technology can be of great help in linking resource efficiency and flexibility in energy supply and demand with innovative, inclusive and more efficient services for citizens and businesses. To realize the European targets for further growth of renewable energy in the energy market, and to exploit both on a European and global level the expected technological opportunities in a sustainable manner, city planners, administrators, universities, entrepreneurs, citizens, and all other relevant stakeholders, need to work together and be the key moving wheel of future EU cities development.Our SolutionIn the light of such a transiting environment, the need for strategies that help cities to smartly integrate technological solutions becomes more and more apparent. Given this condition and the fact that cities can act as large-scale demonstrators of integrated solutions, and want to contribute to the socially inclusive energy and mobility transition, IRIS offers an excellent opportunity to demonstrate and replicate the cities’ great potential. For more information see the HKU Smart Citieswebsite or check out the EU-website.
In leaving the more traditional territories of the concert performance for broader societal contexts, professional musicians increasingly devise music in closer collaboration with their audience rather than present it on a stage. Although the interest for such forms of devising co-creative musicking within the (elderly) health care sector is growing, the work can be considered relatively new. In terms of research, multiple studies have sought to understand the impact of such work on musicians and participants, however little is known about what underpins the musicians’ actions in these settings. With this study, I sought to address this gap by investigating professional musicians’ emerging practices when devising co-creative musicking with elderly people. Three broad concepts were used as a theoretical background to the study: Theory of Practice, co-creative musicking, and Praxialism. Firstly, I used Theory of Practice to help understand the nature of emerging practices in a wider context of change in the field of music and habitus of musicians and participants. Theory of Practice enabled me to consider a practice as “a routinized type of behaviour which consists of several elements, interconnected to one another: forms of bodily activities, forms of mental activities, ‘things’ and their use, a background knowledge in the form of understanding, know-how, states of emotion, and motivational knowledge” (Reckwitz, 2002, p. 249). Secondly, I drew the knowledge from co-creative musicking, which is a concept I gathered from two existing concepts: co-creation and musicking. Musicking (Small, 1998), which considers music as something we do (including any mode of engagement with music), provided a holistic and inclusive way of looking at participation in music-making. The co-creation paradigm encompasses a view on enterprise that consists of bringing together parties to jointly create an outcome that is meaningful to all (Prahalad & Ramaswamy, 2004; Ramaswamy & Ozcan, 2014). The concept served as a lens to specify the jointness of the musicking and challenge issues of power in the engagement of participants in the creative-productive process. Thirdly, Praxialism considers musicking as an activity that encompasses “musical doers, musical doing, something done and contexts in which the former take place” (Elliott, 1995). Praxialism sets out a vision on music that goes beyond the musical work and includes the meanings and values of those involved (Silverman, Davis & Elliott, 2014). The concept allowed me to examine the work and emerging relationships as a result of devising co-creative musicking from an ethical perspective. Given the subject’s relative newness and rather unexplored status, I examined existing work empirically through an ethnographic approach (Hammersley & Atkinson, 2007). Four cases were selected where data was gathered through episodic interviewing (Flick, 2009) and participant observation. Elements of a constructivist Grounded Theory (Charmaz, 2014) were used for performing an abductive analysis. The analysis included initial coding, focused coding, the use of sensitizing concepts (Blumer 1969 in Hammersley, 2013) and memoing. I wrote a thick description (Geertz, 1973) for each case portraying the work from my personal experience. The descriptions are included in the dissertation as one separate chapter and foreshadow the exposition of the analysis in a next chapter. In-depth study of the creative-productive processes of the cases showed the involvement of multiple co-creative elements, such as a dialogical interaction between musicians and audience. However, participants’ contributions were often adopted implicitly, through the musicians interpreting behaviour and situations. This created a particular power dynamic and challenges as to what extent the negotiation can be considered co-creative. The implicitness of ‘making use’ of another person’s behaviour with the other not (always) being aware of this also triggered an ethical perspective, especially because some of the cases involved participants that were vulnerable. The imbalance in power made me examine the relationship that emerges between musicians and participants. As a result of a closer contact in the co-creative negotiation, I witnessed a contact of a highly personal, sometimes intimate, nature. I recognized elements of two types of connections. One type could be called ‘humanistic’, as a friendship in which there is reciprocal care and interest for the other. The other could be seen as ‘functional’, which means that the relationship is used as a resource for providing input for the creative musicking process. From this angle, I have compared the relationship with that of a relationship of an artist with a muse. After having examined the co-creative and relational sides of the interaction in the four cases, I tuned in to the musicians’ contribution to these processes and relationships. I discovered that their devising in practice consisted of a continuous double balancing act on two axes: one axis considers the other and oneself as its two ends. Another axis concerns the preparedness and unpredictability at its ends. Situated at the intersection of the two axes are the musicians’ intentionality, which is fed by their intentions, values and ethics. The implicitness of the co-creation, the two-sided relationship, the potential vulnerability of participants, and the musicians’ freedom in navigating and negotiation, together, make the devising of co-creative musicking with elderly people an activity that involves ethical challenges that are centred around a tension between prioritizing doing good for the other, associated with a eudaimonic intention, and prioritizing values of the musical art form, resembling a musicianist intention. The results therefore call for a musicianship that involves acting reflectively from an ethical perspective. Doctoral study by Karolien Dons
