Reducing energy consumption in urban households is essential for reaching the necessary climate research and policy targets for CO2 reduction and sustainability. The dominant approach has been to invest in technological innovations that increase household energy efficiency. This article moves beyond this approach, first by emphasising the need to prioritise reducing energy demand over increasing energy efficiency and, second, by addressing the challenge of energy consumption at the level of the community, not the individual household. It argues that energy consumption is shaped in and by social communities, which construct consciousness of the energy implications of lifestyle choices. By analysing a specific type of community, a digital community, it looks at the role that communication on online discussion boards plays in the social process of questioning energy needs and shaping a “decent lifestyle”. The article explores three social processes of community interaction around energy practices – coercive, mimetic, and normative – questioning the ways in which they contribute to the activation of energy discursive consciousness. In conclusion, the article reflects on the potential implications of these social processes for future research and interventions aimed at reducing energy demand. To illustrate how the three selected social processes influence one another, the article builds on the results of a research project conducted in Amsterdam, analysing the potential contribution of online discussion boards in shaping energy norms in the Sustainable Community of Amsterdam Facebook group.
During the opening of the Hanze Energy Transition Centre or EnTranCe posters were on display for the King and for the public. These posters where accompanied by the researchers to explain their research in more detail if questions did arise.
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.
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.
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.
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.
