Energiebeheer gericht aanpakken, Het analyseren van doelstellingen, resultaten en impacts van energie- en broeikasgasbeheersprogramma’s in bedrijven (met een samenvatting in het Nederlands): De wereldwijde uitstoot van broeikasgassen moet drastisch worden teruggebracht om de mondiale stijging van de temperatuur tot het relatief veilige niveau van maximaal 2 graden Celsius te beperken. In de komende decennia zal de verbetering van de energie-efficiëntie de belangrijkste strategie zijn voor het verminderen van de energiegerelateerde uitstoot van broeikasgassen. Hoewel er een enorm potentieel is voor verbetering van de energie-efficiëntie, wordt een groot deel daarvan nog niet benut. Dit wordt veroorzaakt door diverse investeringsbarrières die de invoering van maatregelen voor energie-efficiëntie verbetering verhinderen. De invoering van energiemanagement wordt vaak beschouwd als een manier om dergelijke barrières voor energiebesparing te overwinnen. De invoering van energiemanagement in bedrijven kan worden gestimuleerd door de introductie van programma's voor energie-efficiëntie verbetering en vermindering van de uitstoot van broeikasgassen. Deze programma's zijn vaak een combinatie van verschillende elementen zoals verplichtingen voor energiemanagement; (ambitieuze) doelstellingen voor energiebesparing of beperking van de uitstoot van broeikasgassen; de beschikbaarheid van regelingen voor stimulering, ondersteuning en naleving; en andere verplichtingen, zoals openbare rapportages, certificering en verificatie. Tot nu toe is er echter beperkt inzicht in het proces van het formuleren van ambitieuze doelstellingen voor energie-efficiëntie verbetering of het terugdringen van de uitstoot van broeikasgassen binnen deze programma's, in de gevolgen van de invoering van dergelijke programma's op de verbetering van het energiemanagement, en in de impact van deze programma's op energiebesparing of de vermindering van de uitstoot van broeikasgassen. De centrale onderzoeksvraag van dit proefschrift is als volgt geformuleerd: "Wat is de impact van energie- en broeikasgasmanagement programma’s op het verbeteren van het energiemanagement in de praktijk, het versnellen van de energieefficiëntie verbetering en het beperken van de uitstoot van broeikasgassen in bedrijven?".
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 textile and clothing sector belongs to the world’s biggest economic activities. Producing textiles is highly energy-, water- and chemical-intensive and consequently the textile industry has a strong impact on environment and is regarded as the second greatest polluter of clean water. The European textile industry has taken significant steps taken in developing sustainable manufacturing processes and materials for example in water treatment and the development of biobased and recycled fibres. However, the large amount of harmful and toxic chemicals necessary, especially the synthetic colourants, i.e. the pigments and dyes used to colour the textile fibres and fabrics remains a serious concern. The limited range of alternative natural colourants that is available often fail the desired intensity and light stability and also are not provided at the affordable cost . The industrial partners and the branch organisations Modint and Contactgroep Textiel are actively searching for sustainable alternatives and have approached Avans to assist in the development of the colourants which led to the project Beauti-Fully Biobased Fibres project proposal. The objective of the Beauti-Fully Biobased Fibres project is to develop sustainable, renewable colourants with improved light fastness and colour intensity for colouration of (biobased) man-made textile fibres Avans University of Applied Science, Zuyd University of Applied Sciences, Wageningen University & Research, Maastricht University and representatives from the textile industry will actively collaborate in the project. Specific approaches have been identified which build on knowledge developed by the knowledge partners in earlier projects. These will now be used for designing sustainable, renewable colourants with the improved quality aspects of light fastness and intensity as required in the textile industry. The selected approaches include refining natural extracts, encapsulation and novel chemical modification of nano-particle surfaces with chromophores.
“Being completely circular by 2050” that is the goal for the Dutch economy. The transition towards the circular and biobased economy for energy and materials is essential to reach that goal. Sustainably produced materials based on renewable sources like biomass should be developed. One of the industries which recognizes the need for transition is the building industry. Currently, there are a couple of biobased building concepts available which claim to be more than 95% biobased. Since the current resins and adhesives, used to produce panel boards (like cross laminated timber (CLT)), are all produced synthetically, one of the missing links for the building industry to become 100% biobased are biobased resins and adhesives (and binders). In literature, there are several solutions described for resins/adhesives/binders which are based on the biomolecules lignin and cellulose which are abundantly present in fibrous biomass, but these products are not (yet) available on the market. At the same time, there are several fibrous biomass side streams available for which higher added value applications are demanded. These side streams are perfect sources of lignin and cellulose and are, therefore, very suitable sources to form the basis for biobased resins/adhesives/binders. However, they need modification to obtain the desired functionalities. The problem statement of this project, based on the request for valorization of fibrous side streams and the need for biobased building materials, is “How can we valorize fibrous biomass (side streams) into biobased building applications.” This problem statement is translated into the research goal. The aim of this research is to develop a biobased resin, adhesive or binder for the production of panel boards based on the side streams of fibrous/lignocellulosic biomass which meets the requirement of the building industry with respect to VOC emissions, and water resistance so that it contributes to a healthy living environment.
AANLEIDING In het RAAK-MKB project ‘Gelijkspanning breng(t) je verder’ heeft De Haagse Hogeschool, specifiek de opleiding Elektrotechniek, ervaren dat de opkomst van het onderwerp ‘Gelijkspanning’ (ook wel DC) in het beroepenveld sterk samenhangt met ontwikkelingen in het vakgebied van ‘Vermogenselektronica’ of ‘Power Eletronics’. Het beroepenveld vraagt steeds vaker om steeds meer kennis op dit vakgebied, in het kader van bijvoorbeeld de energietransitie, Smart Grids, Internet-of-Things etc. Om deze kennis op een goed gestructureerde wijze over te dragen aan studenten, moeten er een aantal belemmeringen worden weggewerkt. Een van deze belemmeringen is de beperkte beschikbaarheid van kennis; het vakgebied is relatief nieuw en nog sterk in ontwikkeling. Binnen De Haagse Hogeschool is door de opleiding Elektrotechniek (met kennis van de nog weg te werken belemmeringen) de bewuste keuze gemaakt om zich binnen Nederland te willen profileren met het onderwerp ‘Gelijkspanning’. Vanuit het eerdere RAAK-MKB project ‘Gelijkspanning breng(t) je verder’ werden hiertoe een eerste vak en practicum ontwikkeld: Vermogenselektronica 1. Hierin worden beginselen van DC-DC omvormers behandeld. DC-DC omvormers zorgen voor het transformeren van DC-spanningen, om energie bij hoge spanningen en dus lage verliezen te kunnen transporteren. Vanaf het huidige collegejaar (2015-2016) is ook een tweede vak op dit gebied toegevoegd aan het curriculum: Vermogenselektronica 2: hierin worden DC-AC omvormers op hoofdlijnen behandeld. Deze omvormers zorgen ervoor dat veel gebruikte types motoren aangedreven kunnen worden met gelijkspanning. Deze hoofdlijnen staan in de ogen van het beroepenveld nog (te) ver af van toepassingen waarmee zij werken. Daarbij moet gedacht worden aan bijvoorbeeld elektrische mobiliteit (specifieke types motoren), verlichting (DC-DC), distributietechnieken (DC-DC op hogere vermogens) of slimme netten (integratie van energietechniek, communicatietechnologie en regeltechniek / embedded systems). DOELSTELLING Het doel van het project is het opstellen van een implementatiewijze ter verdere invulling van de onderwerpen ‘Gelijkspanning’ en ‘Vermogenselektronica’ in het curriculum van de opleiding Elektrotechniek voor de teamleider van Elektrotechniek van De Haagse Hogeschool om de gewenste profilering te kunnen realiseren. ACTIVITEITEN Vanuit de curriculum commissie van de opleiding Elektrotechniek wordt opdracht gegeven aan een apart team om het implementatievoorstel voor te bereiden. Hierin werken twee docent/onderzoekers samen met de teamleider en enkele extern specialisten. In vijf opeenvolgende stappen wordt op een top-down manier gewerkt aan 1. Formuleren competenties voor DC 2. Hoofdstromen curriculum inrichten 3. Uitwerken vakinhoudelijke gebieden Elektrotechniek (‘leeg vel papier’) 4. Koppelen opzet aan bezetting en kennis in het team en bij partners 5. Voorbereiden besluitvorming RESULTAAT Op deze wijze wordt een heldere visie ontwikkeld op het benodigde onderwijs om het onderwerp gelijkspanning gestructureerd aan te kunnen bieden. Daarbij gaat het om vakinhoudelijke kennis in vakken, met bijbehorende practica en projecten. Om deze kennis goed aan te bieden wordt nadrukkelijk ook de samenwerking met andere kennisinstellingen (zoals Zuyd Hogeschool en de TU-Delft) gezocht.
