The paper briefly describes the outcome of two inventories: 1) an inventory based on fact sheets about daily use and energy consumption of historical buildings (mainly relative small churches) in the North of the Netherlands, and; 2) an inventory based on interviews with owners of historical buildings about motives to reduce energy consumption.
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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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Thermal comfort is determined by the combined effect of the six thermal comfort parameters: temperature, air moisture content, thermal radiation, air relative velocity, personal activity and clothing level as formulated by Fanger through his double heat balance equations. In conventional air conditioning systems, air temperature is the parameter that is normally controlled whilst others are assumed to have values within the specified ranges at the design stage. In Fanger’s double heat balance equation, thermal radiation factor appears as the mean radiant temperature (MRT), however, its impact on thermal comfort is often ignored. This paper discusses the impacts of the thermal radiation field which takes the forms of mean radiant temperature and radiation asymmetry on thermal comfort, building energy consumption and air-conditioning control. Several conditions and applications in which the effects of mean radiant temperature and radiation asymmetry cannot be ignored are discussed. Several misinterpretations that arise from the formula relating mean radiant temperature and the operative temperature are highlighted, coupled with a discussion on the lack of reliable and affordable devices that measure this parameter. The usefulness of the concept of the operative temperature as a measure of combined effect of mean radiant and air temperatures on occupant’s thermal comfort is critically questioned, especially in relation to the control strategy based on this derived parameter. Examples of systems which deliver comfort using thermal radiation are presented. Finally, the paper presents various options that need to be considered in the efforts to mitigate the impacts of the thermal radiant field on the occupants’ thermal comfort and building energy consumption.
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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%.
Synthetic ultra-black (UB) materials, which demonstrate exceptionally high absorbance (>99%) of visible light incident on their surface, are currently used as coatings in photovoltaic cells and numerous other applications. Most commercially available UB coatings are based on an array of carbon nanotubes, which are produced at relatively high temperature and result in numerous by-products. In addition, UB nanotube coatings require harsh application conditions and are very susceptible to abrasion. As a result, these coatings are currently obtained using a manufacturing process with relatively high costs, high energy consumption and low sustainability. Interestingly, an UB coating based on a biologically derived pigment could provide a cheaper and more sustainable alternative. Specifically, GLO Biotics proposes to create UB pigment by taking a bio-mimetic approach and replicate structures found in UB deep-sea fish. A recent study[1] has actually shown that specific fish have melanosomes in their skin with particular dimensions that allow absorption of up to 99.9% of incident light. In addition to this, recent advances in bacterial engineering have demonstrated that it is possible to create bacteria-derived melanin particles with very similar dimensions to the melanosomes in aforementioned fish. During this project, the consortium partners will combine both scientific observations in an attempt to provide the proof-of-concept for developing an ultra-black coating using bacteria-derived melanin particles as bio-based, sustainable pigment. For this, Zuyd University of Applied Sciences (Zuyd) and Maastricht University (UM) collaborate with GLO Biotics in the development of the innovative ‘BLACKTERIA’ UB coating technology. The partners will attempt at engineering an E. coli expression system and adapt its growth in order to produce melanin particles of desired dimensions. In addition, UM will utilize their expertise in industrial coating research to provide input for experimental set-up and the development of a desired UB coating using the bacteria-derived melanin particles as pigment.
Buildings are responsible for approximately 40% of energy consumption and 36% of carbon dioxide (CO2) emissions in the EU, and the largest energy consumer in Europe (https://ec.europa.eu/energy). Recent research shows that more than 2/3 of all CO2 is emitted during the building process whereas less than 1/3 is emitted during use. Cement is the source of about 8% of the world's CO2 emissions and innovation to create a distributive change in building practices is urgently needed, according to Chatham House report (Lehne et al 2018). Therefore new sustainable materials must be developed to replace concrete and fossil based building materials. Lightweight biobased biocomposites are good candidates for claddings and many other non-bearing building structures. Biocarbon, also commonly known as Biochar, is a high-carbon, fine-grained solid that is produced through pyrolysis processes and currently mainly used for energy. Recently biocarbon has also gained attention for its potential value with in industrial applications such as composites (Giorcellia et al, 2018; Piri et.al, 2018). Addition of biocarbon in the biocomposites is likely to increase the UV-resistance and fire resistance of the materials and decrease hydrophilic nature of composites. Using biocarbon in polymer composites is also interesting because of its relatively low specific weight that will result to lighter composite materials. In this Building Light project the SMEs Torrgas and NPSP will collaborate with and Avans/CoE BBE in a feasibility study on the use of biocarbon in a NPSP biocomposite. The physicochemical properties and moisture absorption of the composites with biocarbon filler will be compared to the biocomposite obtained with the currently used calcium carbonate filler. These novel biocarbon-biocomposites are anticipated to have higher stability and lighter weight, hence resulting to a new, exciting building materials that will create new business opportunities for both of the SME partners.
