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.
DOCUMENT
Electrification of transportation, communication, working and living continues worldwide. Televisions, telephones, servers are an important part of everyday life. These loads and most sustainable sources as well, have one thing in common: Direct Current. The Dutch research and educational programme ‘DC – road to its full potential’ studies the impact of feeding these appliances from a DC grid. An improvement in energy efficiency is expected, other benefits are unknown and practical considerations are needed to come to a proper comparison with an AC grid. This paper starts with a brief introduction of the programme and its first stages. These stages encompass firstly the commissioning, selection and implementation of a safe and user friendly testing facility, to compare performance of domestic appliances when powered with AC and DC. Secondly, the relationship between the DC-testing facility and existing modeling and simulation assignments is explained. Thirdly, first results are discussed in a broad sense. An improved energy efficiency of 3% to 5% is already demonstrated for domestic appliances. That opens up questions for the performance of a domestic DC system as a whole. The paper then ends with proposed minor changes in the programme and guidelines for future projects. These changes encompass further studying of domestic appliances for product-development purposes, leaving less means for new and costly high-power testing facilities. Possible gains are 1) material and component savings 2) simpler and cheaper exteriors 3) stable and safe in-house infrastructure 4) whilst combined with local sustainable generation. That is the road ahead. 10.1109/DUE.2014.6827758
DOCUMENT
Sustainability has become an important blueprint to achieve a better future for all, and as part of this process, nations are called to accelerate an energy transition towards clean energy solutions. However, an often-neglected pillar is educating individuals on the benefits and challenges of energy efficiency and renewable energy, especially among young people. Their support and willingness to use clean energies will be a significant driver in short, medium and long term. However, reality shows that attention from youth on these issues has not been sufficient yet. Formal education settings become therefore a key place to educate youth in the energy transition. In search of innovative approaches, game-based learning is gaining popularity among scholars and practitioners; it can contribute to content development of complex issues by integrating insights from different disciplines in an interactive, fun and engaging manner.In this context, we would like to present “the We-Energy Game” as an innovative educational strategy which makes use of game-based learning to create understanding on the challenges in the provision of affordable energy from renewable sources for an entire town. During the game, players negotiate, from their respective roles, which energy source they want to employ and on which location, with the goal to make a village or city energy neutral. The game has been played by students in higher education institutions in The Netherlands.In addition to introducing the game, a study is presented on the effects of the game on students´ awareness on the energy transition, self-efficacy -the feeling that they can contribute to a sustainable energy transition in their towns by themselves- and collective efficacy -the feeling that they can contribute to a sustainable energy transition in their towns together with their community-. For that purpose, we conducted a survey with 100 bachelor (Dutch and international) students aged between 18 and 30 years old, at Hanze University of Applied Sciences, before and after playing the game. We also conducted a group discussion with a smaller group of students to understand their opinion about the game. From the survey, results reveal an increase in awareness about the energy transition, as well as (slightly higher) collective efficacy compared to self-efficacy. From the group discussion, findings reveal that the game makes students reflect on the complexity of the process and need for collaboration among different stakeholders. It also shows how educational games have still a long way to go to achieve the high levels of engagement of commercial games, despite the fact that students still preferred to have this type of interactive practice rather than a traditional class characterized by a unidirectional transmission of information. Different implications must be taken into account for educators when interested in implementing game-based learning in class, including immediate feedback, appropriate length of gameplay during class, and time for a reflection and critical thinking after playing the game.
DOCUMENT
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.
Due to the existing pressure for a more rational use of the water, many public managers and industries have to re-think/adapt their processes towards a more circular approach. Such pressure is even more critical in the Rio Doce region, Minas Gerais, due to the large environmental accident occurred in 2015. Cenibra (pulp mill) is an example of such industries due to the fact that it is situated in the river basin and that it has a water demanding process. The current proposal is meant as an academic and engineering study to propose possible solutions to decrease the total water consumption of the mill and, thus, decrease the total stress on the Rio Doce basin. The work will be divided in three working packages, namely: (i) evaluation (modelling) of the mill process and water balance (ii) application and operation of a pilot scale wastewater treatment plant (iii) analysis of the impacts caused by the improvement of the process. The second work package will also be conducted (in parallel) with a lab scale setup in The Netherlands to allow fast adjustments and broaden evaluation of the setup/process performance. The actions will focus on reducing the mill total water consumption in 20%.
Designing with the Sun is a KIEM-GoCI explorative research project on the theme Energy Transition and Sustainability. The project is aimed at network and agenda building and design research that explores new (cultural) practices of renewable energy consumption, based on a shift from ‘energy blindness’ to ‘energy awareness’. Up until now the solar industry has been propelled forward by technical innovations, offering mostly pragmatic, economic benefits to consumers. Innovation in this field mostly concerns making solar panels more efficient and less costly. However, to succeed, the energy transition also needs new cultural practices. These practices should reflect the ways renewables are different from fossil fuels. For solar, this means using more direct solar energy, when the sun is there, and being able to adapt to periods of low energy. Currently, consumers are mostly ‘blind’ to the infrastructure behind fossil-based energy. However, for energy sources such as solar and wind ‘awareness’ of their availability becomes more important. What could such an awareness look or feel like? How can it be enacted? And how can a change in practice that is more attuned to availability be experienced positively? Solar companies see opportunities in using design to help build motivating practices and narratives within the solar field, enabling awareness through personal relationships between consumer and solar energy. However, the knowledge of how to get there is lacking. In a research-through-design trajectory, and together with partners from the Creative Industries, Designing with the Sun aims to explore new ways of relating citizens to solar energy. Ultimately, these insights should enable the newly emerging field of solar design to contribute to the emergence of more sustainable and rewarding energy awareness and practices.
