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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To help the cities realise their positive energy districts, the Atelier project includes a capacity building programme, in which professionals in the partner cities can learn from each other and experts how to make their ambitions come true. Amsterdam University of Applied Sciences is one of the partners to design the training, learning, and coaching activities. Here are five hard questions to give an impression on what kind of knowledge and expertise is needed.
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Sustainable urban mobility is an established target of policy making and planning in Europe. It is associated with, among others, better air quality, less noise disturbance, increased safety and quality of public space. In this regard, one of the EU Commission’s main tools to achieve sustainable urban mobility, through Sustainable Urban Mobility Plans (SUMP), require the explicit integration of Monitoring and Evaluation (M&E). Yet, European cities face common barriers when it comes to materialising M&E in practice. To avoid or overcome these barriers, this paper argues for integrating Capacity Building (CB). We draw this conclusion based on experiences made during the M&E and CB of the Horizon 2020 Project ‘Metamorphosis’. We report our experiences, rating different monitoring indicators used for the evaluation of measures transforming car-oriented neighbourhoods into children-friendly neighbourhoods in seven European cities. We then give advice on how to design and integrate CB for a feasible M&E scheme.
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This paper investigates the limits and efficacies of the Fiber Reinforced Polymer (FRP) material for strengthening mid-rise RC buildings against seismic actions. Turkey, the region of the highest seismic risk in Europe, is chosen as the case-study country, the building stock of which consists in its vast majority of mid-rise RC residential and/or commercial buildings. Strengthening with traditional methods is usually applied in most projects, as ordinary construction materials and no specialized workmanship are required. However, in cases of tight time constraints, architectural limitations, durability issues or higher demand for ductile performance, FRP material is often opted for since the most recent Turkish Earthquake Code allows engineers to employ this advanced-technology product to overcome issues of inadequate ductility or shear capacity of existing RC buildings. The paper compares strengthening of a characteristically typical mid-rise Turkish RC building by two methods, i.e., traditional column jacketing and FRP strengthening, evaluating their effectiveness with respect to the requirements of the Turkish Earthquake Code. The effect of FRP confinement is explicitly taken into account in the numerical model, unlike the common procedure followed according to which the demand on un-strengthened members is established and then mere section analyses are employed to meet the additional demands.
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Erasmus Plus project SUSWELL aims to improve Health and Well-being in Kosovo and other Eastern European countries. We asked projectleader Paul Beenen from Hanzehogeschool Groningen / Hanze University of Applied Sciences Groningen 4 main questions about his drive and motivation for SUSWELL.
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In November 2019, scholars and practitioners from ten higher education institutions celebrated the launch of the iKudu project. This project, co-funded by Erasmus [1], focuses on capacity development for curriculum transformation through internationalisation and development of Collaborative Online International Learning (COIL) virtual exchange. Detailed plans for 2020 were discussed including a series of site visits and face-to-face training. However, the realities of the COVID-19 pandemic disrupted the plans in ways that could not have been foreseen and new ways of thinking and doing came to the fore. Writing from an insider perspective as project partners, in this paper we draw from appreciative inquiry, using a metaphor of a mosaic as our identity, to first provide the background on the iKudu project before sharing the impact of the pandemic on the project’s adapted approach. We then discuss how alongside the focus of iKudu in the delivery of an internationalised and transformed curriculum using COIL, we have, by our very approach as project partners, adopted the principles of COIL exchange. A positive impact of the pandemic was that COIL offered a consciousness raising activity, which we suggest could be used more broadly in order to help academics think about international research practice partnerships, and, as in our situation, how internationalised and decolonised curriculum practices might be approached. 1. KA2 Erasmus+ Cooperation for innovation and the exchange of good practices (capacity building in the field of Higher Education)
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