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Contribution of Physical Education and Active Transport to Energy Expenditure in Adolescents

It has been suggested that physical education (PE) and active transport can make a meaningful contribution to children's physical activity (PA) levels. However, data on the contribution these activities to total PA is scarce, and PE's contribution to total physical activity energy expenditure (PAEE) has to our knowledge never been determined. This is probably explained by the methodological complexity of determining PAEE (Welk, 2002). In this paper, we present the first data of an ongoing study using combined heart rate monitoring and accelerometry, together with activity diaries. Over the six measurement days, PE contributed 5% to total PAEE, and 16% to school-related PAEE, whereas active transportation had a much larger contribution.

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Contribution of Physical Education and Active Transport to Energy Expenditure in Adolescents

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Onderzoeksagenda Nationaal Lectorenplatform Urban Energy

Uit het rapport: "Deze onderzoeksagenda is tot stand gebracht door de lectoren die samenwerken in het Nationaal Lectoren Platform Urban Energy. Alle betrokkenen bij het platform zijn in staat gesteld om bij te dragen aan de tekst, speciale dank daarbij voor de bijdragen en commentaren vanuit de TKI Urban Energy en de HCA topsector Energie."

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Onderzoeksagenda Nationaal Lectorenplatform Urban Energy

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Verslag provincie Utrecht

Op 4 december is in Eemnes een We-Energy Game workshop gegeven. Hieruit is ook een verslag opgemaakt waarbij de resultaten werden teruggekoppeld aan de opdrachtgever.

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Building and building systems energy prediction models to enhance energy flexibility control

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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Building and building systems energy prediction models to enhance energy flexibility control

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Contribution of Physical Education and Active Transport to Energy Expenditure in Dutch Adolescents

Introduction: It has been suggested that physical education (PE) can make a meaningful contribution to children's physical activity (PA) levels. The amount of moderate-to-vigorous physical activity (MVPA) in PE has been quantified in various manners, including heart rate monitoring and direct observation (Fairclough & Stratton, 2005). However, data on the contribution of PE to total PA is scarce, and PE's contribution to total physical activity energy expenditure (PAEE) has to our knowledge never been determined. This is probably explained by the methodological complexity of determining PAEE (Welk, 2002). However, the fairly recent emergence of combined sensing methodology allows for low-invasive measurement of PAEE in free-living conditions. In this paper, we present the first data of an ongoing study using combined heart rate monitoring and accelerometry, together with activity diaries. We assessed the contribution of PE and other school-related activity to PAEE and MVPA. Methods: Nineteen secondary school students (16 ± 0,7 yrs, BMI 22 ± 4) were included after they and their parents had consented. All had 100 minutes of scheduled PE per week. Actiheart monitors (CamNtech, Cambridge, UK) were used to determine PAEE on four weekdays and two weekend days consecutively. Actiheart monitors combine a heart rate monitor and an uniaxial accelerometer in a single 10 gram unit, that is applied to the chest with electrodes. Using a step test, an individual heart rate-energy expenditure relationship was determinded in each subject. Through a validated branched equation model (Brage, S. et al., 2007), energy expenditure was calculated. In addition, subjects kept an activity diary for the same six-day period. They recorded predefined activities including PE and active transport. These activities were then retraced to the Actiheart data by visual inspection. Results: Table 1 shows the (contribution of) PE, and school-related active transport to PAEE, while table 2 shows similar data for MVPA. Data are mean (± SD). Table 1: PAEE for PE, and active transport (AT). Table 2: MVPA for PE and active transport (AT). PAEE (KJ) % of total % of school PE 805(474) 5(4) 16(7) AT 1698(1033) 11(6) 31(11) MVPA (min) % of total % of school PE 36(19) 9(8) 22(11) AT 90(56) 20(11) 48(14) Over all six days, the physical activity level (PAL, which is total EE/Resting EE) was 1,54 ± 0,12; total MVPA was 472 min ± 179, and total PAEE 16262 KJ ± 5267. PAEE at school (4 days, including AT) was 5311 ± 3065 KJ, amounting to 34 % of total PAEE during the six measurement days. Students accumulated 179 ± 77 minutes of MVPA at school, which was 38% of total MVPA. Discussion: To our knowledge, this is the first study to present data on PE's contribution to total physical activity energy expenditure. Over the six measurement days, PE contributed 5% to total PAEE, and 16% to school-related PAEE. This was substantially less than the amount of energy expended for active transport to and from school. However, it should be noted that in the Netherlands, the vast majority of secondary school students cycle to school. And while PE was scheduled on one day per week in all of the measured students, active transport takes place on all school days. The total amount of MVPA accumulated at school was 179 minutes. With adolescent physical activity guidelines generally recommending 60 min of MVPA per day, i.e. 420 minutes per week, this means that school-related PA covered ~43% of this. PE provided 36 minutes to this total, all on one day. It could be argued that daily PE could potentially provide a substantial amount of MVPA. But with current time allocated to PE in the curriculum, its contribution to physical activity guidelines and PAEE is quite modest. The preliminary data presented here reflect a small subsample of a larger study that is still in progress. Therefore, care should be taken not to interpret these outcomes as representative for the whole of the Netherlands. However, they do provide a first indication for the order of magnitude of the contribution of PE and school-related activity to total PAEE. References: Fairclough, S. J. & Stratton, G. (2005) Physical Activity Levels in Middle and High School Physical Education: A Review. Pediatric Exercise Science, 17, 217. Welk, G. J. (2002) Physical activity assessments for health-related research, Champaign, Ill.; United States, Human Kinetics. Brage, S., Ekelund, U., Brage, N. Hennings, M.A., Froberg, K., Franks, P.W., Wareham. N.J. (2007). Hierarchy of individual calibration levels for heart rate and accelerometry to measure physical activity. J Appl Physiol, 103, (682-692)

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People 6

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Eke Eijgelaar

Researcher

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Frank Pierie

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Jan Bekkering

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Nina Nesterova

Professor Sustainable Development of Tourism and Transport

Projects 22

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Carbon management for tour operators (CARMATOP)

Client: Foundation Innovation Alliance (SIA - Stichting Innovatie Alliantie) with funding from the ministry of Education, Culture and Science (OCW) Funder: RAAK (Regional Attention and Action for Knowledge circulation) The RAAK scheme is managed by the Foundation Innovation Alliance (SIA - Stichting Innovatie Alliantie) with funding from the ministry of Education, Culture and Science (OCW). Early 2013 the Centre for Sustainable Tourism and Transport started work on the RAAK-MKB project ‘Carbon management for tour operators’ (CARMATOP). Besides NHTV, eleven Dutch SME tour operators, ANVR, HZ University of Applied Sciences, Climate Neutral Group and ECEAT initially joined this 2-year project. The consortium was later extended with IT-partner iBuildings and five more tour operators. The project goal of CARMATOP was to develop and test new knowledge about the measurement of tour package carbon footprints and translate this into a simple application which allows tour operators to integrate carbon management into their daily operations. By doing this Dutch tour operators are international frontrunners.Why address the carbon footprint of tour packages?Global tourism contribution to man-made CO2 emissions is around 5%, and all scenarios point towards rapid growth of tourism emissions, whereas a reverse development is required in order to prevent climate change exceeding ‘acceptable’ boundaries. Tour packages have a high long-haul and aviation content, and the increase of this type of travel is a major factor in tourism emission growth. Dutch tour operators recognise their responsibility, and feel the need to engage in carbon management.What is Carbon management?Carbon management is the strategic management of emissions in one’s business. This is becoming more important for businesses, also in tourism, because of several economical, societal and political developments. For tour operators some of the most important factors asking for action are increasing energy costs, international aviation policy, pressure from society to become greener, increasing demand for green trips, and the wish to obtain a green image and become a frontrunner among consumers and colleagues in doing so.NetworkProject management was in the hands of the Centre for Sustainable Tourism and Transport (CSTT) of NHTV Breda University of Applied Sciences. CSTT has 10 years’ experience in measuring tourism emissions and developing strategies to mitigate emissions, and enjoys an international reputation in this field. The ICT Associate Professorship of HZ University of Applied Sciences has longstanding expertise in linking varying databases of different organisations. Its key role in CARMATOP was to create the semantic wiki for the carbon calculator, which links touroperator input with all necessary databases on carbon emissions. Web developer ibuildings created the Graphical User Interface; the front end of the semantic wiki. ANVR, the Dutch Association of Travel Agents and Tour operators, represents 180 tour operators and 1500 retail agencies in the Netherlands, and requires all its members to meet a minimum of sustainable practices through a number of criteria. ANVR’s role was in dissemination, networking and ensuring CARMATOP products will last. Climate Neutral Group’s experience with sustainable entrepreneurship and knowledge about carbon footprint (mitigation), and ECEAT’s broad sustainable tourism network, provided further essential inputs for CARMATOP. Finally, most of the eleven tour operators are sustainable tourism frontrunners in the Netherlands, and are the driving forces behind this project.

Finished

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CHANGE

To reach the European Green Deal by 2050, the target for the road transport sector is set at 30% less CO2 emissions by 2030. Given the fact that heavy-duty commercial vehicles throughout Europe are driven nowadays almost exclusively on fossil fuels it is obvious that transition towards reduced emission targets needs to happen seamlessly by hybridization of the existing fleet, with a continuously increasing share of Zero Emission vehicle units. At present, trailing units such as semitrailers do not possess any form of powertrain, being a missed opportunity. By introduction of electrically driven axles into these units the fuel consumption as well as amount of emissions may be reduced substantially while part of the propulsion forces is being supplied on emission-free basis. Furthermore, the electrification of trailing units enables partial recuperation of kinetic energy while braking. Nevertheless, a number of challenges still exist preventing swift integration of these vehicles to daily operation. One of the dominating ones is the intelligent control of the e-axle so it delivers right amount of propulsion/braking power at the right time without receiving detailed information from the towing vehicle (such as e.g. driver control, engine speed, engine torque, or brake pressure, …etc.). This is required mainly to ensure interoperability of e-Trailers in the fleets, which is a must in the logistics nowadays. Therefore the main mission of CHANGE is to generate a chain of knowledge in developing and implementing data driven AI-based applications enabling SMEs of the Dutch trailer industry to contribute to seamless energetic transition towards zero emission road freight transport. In specific, CHANGE will employ e-Trailers (trailers with electrically driven axle(s) enabling energy recuperation) connected to conventional hauling units as well as trailers for high volume and extreme payload as focal platforms (demonstrators) for deployment of these applications.

Ongoing

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Circular packaging choices: reconciling consumer perceptions in product packaging design.

The production, use, disposal and recovery of packaging not only generates massive volumes of waste, it also consumes raw materials, water and energy (Fitzpatrick et al. 2012). Simultaneously, consumers have shown an increasing interest in products incorporating sustainable and social attributes (Kletzan et al., 2006). As a result, environmentally friendly packaging, also called ecofriendly or sustainable packaging, has become mainstream. In this context, packaging is more than just ensuring the product's protection and easing transportation, it is also a communicative tool (Palmer, 2000) and it becomes associated with multiple drivers of the purchasing process. Consequently, companies face pressure to innovate responding to consumer demands, and focusing on sustainable solutions that reduce harmful materials and favour green alternatives for both, the product and the packaging. Although the above has triggered research on consumer choice for sustainable products and alternatives on sustainable packaging, the relation between sustainable packaging and consumer behaviour remains underexplored. This research unpacks this relationship, i.e., empirically verifies which dimensions (recyclability, biodegradability, reusability) of sustainable packaging are perceived and valued by consumers. Put differently, this research investigates consumer behaviour towards the functions of sustainable packaging in terms of product protection, convenience, reliability of information and promotion, and scrutinises the perceived credibility of the associated ethical responsibility claims. It aims to identify those packaging materials and/or sustainability characteristics perceived as more sustainable by consumers as well as the factors influencing actual consumer choice towards sustainable packaged products. We aim to gain more insights in the perceptual frame that different types of consumers apply when exposed to sustainable packaging. To this end, we will make use of revealed preference methods to measure consumer valuations of sustainable packaged products. This game-theoretic approach should provide a more complete depiction of consumers' perceptions and preferences.

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CrossReTour

Cross-Re-Tour supports European tourism SME while implementing digital and circular economy innovations. The three year project promotes uptake and replication by tourism SMEs of tools and solutions developed in other sectors, to mainstream green and circular tourism business operations.At the start of the project existing knowledge-gaps of tourism SMEs will be researched through online dialogues. This will be followed by a market scan, an overview of existing state of the art solutions to digital and green constraints in other economic sectors, which may be applied to tourism SME business operations: water, energy, food, plastic, transport and furniture /equipment. The scan identifies best practices from other sectors related to nudging of clients towards sustainable behaviour and nudging of staff on how to best engage with new tourism market segments.The next stage of the project relates to two design processes: an online diagnostic tool that allows for measuring and assessing (160) SME’s potential to adapt existing solutions in digital and green challenges, developed in other economic sectors. Next to this, a knowledge hub, addresses knowledge constraints and proposes solutions, business advisory services, training activities to SMEs participating. The hub acts as a matchmaker, bringing together 160 tourism SMEs searching for solutions, with suppliers of existing solutions developed in other sectors. The next key activity is a cross-domain open innovation programme, that will provide 80 tourism SMEs with financial support (up to EUR 30K). Examples of partnerships could be: a hotel and a supplier of refurbished matrasses for hospitals; a restaurant and a supplier of food rejected by supermarkets, a dance event organiser and a supplier of refurbished water bottles operating in the cruise industry, etc.The 80 cross-domain partnerships will be supported through the knowledge hub and their business innovation advisors. The goal is to develop a variety of innovative partnerships to assure that examples in all operational levels of tourism SMEs.The innovation projects shall be presented during a show-and-share event, combined with an investors’ pitch. The diagnostic tool, market scan, knowledge hub, as well as the show and share offer excellent opportunities to communicate results and possible impact of open innovation processes to a wider international audience of destination stakeholders and non-tourism partners. Societal issueSupporting the implementation of digital and circular economy solutions in tourism SMEs is key for its transition towards sustainable low-impact industry and society. Benefit for societySolutions are already developed in other sectors but the cross-over towards tourism is not happening. The project bridges this gap.

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DC Onderwijs

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.

Finished

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Drones for Sustainable Port Logistics

The research, supported by our partners, sets out to understand the drivers and barriers to sustainable logistics in port operations using a case study of drone package delivery at Rotterdam Port. Beyond the technical challenges of drone technology as an upcoming technology, it needs to be clarified how drones can operate within a port ecosystem and how they could contribute to sustainable logistics. KRVE (boatmen association), supported by other stakeholders of Rotterdam port, approached our school to conduct exploratory research. Rotterdam Port is the busiest port in Europe in terms of container volume. Thirty thousand vessels enter the port yearly, all needing various services, including deliveries. Around 120 packages/day are delivered to ships/offices onshore using small boats, cars, or trucks. Deliveries can take hours, although the distance to the receiver is close via the air. Around 80% of the packages are up to 20kg, with a maximum of 50kg. Typical content includes documents, spare parts, and samples for chemical analysis. Delivery of packages using drones has advantages compared with traditional transport methods: 1. It can save time, which is critical to port operators and ship owners trying to reduce mooring costs. 2. It can increase logistic efficiency by streamlining operations. 3. It can reduce carbon emissions by limiting the use of diesel engines, boats, cars, and trucks. 4. It can reduce potential accidents involving people in dangerous environments. The research will highlight whether drones can create value (economic, environmental, social) for logistics in port operations. The research output links to key national logistic agenda topics such as a circular economy with the development of innovative logistic ecosystems, energy transition with the reduction of carbon emissions, societal earning potential where new technology can stimulate the economy, digitalization, key enabling technology for lean operations, and opportunities for innovative business models.

Ongoing

Editorials 1

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Van poep tot plastic? Het kan!

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Contribution of Physical Education and Active Transport to Energy Expenditure in Adolescents

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