The municipality of Apeldoorn had polled the interest among its private home-owners to turn their homes energy neutral. Based on the enthusiastic response, Apeldoorn saw the launch of the Energy Apeldoorn (#ENEXAP) in 2011. Its goal was to convert to it technically and financially possible for privately owned homes to be refurbished and to energy neutral, taking the residential needs and wishes from occupants as the starting point. The project was called an Expedition, because although the goal was clear, the road to get there wasn’t. The Expedition team comprised businesses, civil-society organisations, the local university of applied sciences, the municipality of Apeldoorn, and of course, residents in a central role. The project was supported by Platform31, as part of the Dutch government’s Energy Leap programme. The #ENEXAP involved 38 homes, spread out through Apeldoorn and surrounding villages. Even though the houses were very diverse, the group of residents was quite similar: mostly middle- aged, affluent people who highly value the environment and sustainability. An important aspect of the project was the independent and active role residents played. In collaboration with businesses and professionals, through meetings, excursions, workshops and by filling in a step- by-step plan on the website, the residents gathered information about their personal situation, the energy performance of their home and the possibilities available for them to save and generate energy themselves. Businesses were encouraged to develop an integrated approach for home-owners, and consortia were set up by businesses to develop the strategy, products and services needed to meet this demand. On top of making minimal twenty from the thirty-eight houses in the project energy neutral, the ultimate goal was to boost the local demand for energy- neutral refurbishment and encourage an appropriate supply of services, opening up the (local) market for energy neutral refurbishment. This paper will reflect on the outcomes of this collective in the period 2011-2015.
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.
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.
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.
One of the mission-driven innovation policies of the Netherlands is energy transition which sets, among others, the challenge for a carbon-neutral built environment in 2050. Around 41% of Dutch houses do not yet have a registered energy label, and approximately 31% of the registered houses have label C or lower. This calls for action within the housing renovation industry. Bound to the 70 percent rule, a renovation plan requires full (or at least 70 percent) agreement on the renovation between relevant parties, including residents. In practice, agreement indicators focus mostly on economic and energy aspects. When indicators include people’s needs and preferences, it is expected to speed participation and agreement, increasing residents’ satisfaction and enhances the trust in public institutions. Tsavo was founded in 2015 to organise the sustainability of buildings for ambitious clients. Its sustainability process aims to accelerate renovation by keeping at their core value the social needs and preferences of residents. In this project Tsavo and TU Delft work together to optimise the sustainability process so, it includes everyone’s input and results in a sustainability plan that represents everyone. Tsavo’s role will be key in keeping the balance between both a sustainable renovation service that is cheaper and fast yet also attractive and with an impact on the quality of living. In this project, Tsavo’s sustainable renovation projects will be used to implement methods that focus on increasing participation and residents’ satisfaction. TU Delft will explore principles of attractive, accessible and representative activities to stimulate residents to decide on a renovation plan that is essential and meaningful to all.
Wat is de mogelijke rol van lokale duurzame energiesystemen en –initiatieven in de overgang naar een duurzame samenleving? En hoe kunnen op lokale toepassing gerichte innovaties worden ontwikkeld en toegepast op een zodanige manier dat deze bij lokale systemen en initiatieven aansluiten?Deze vragen staan centraal in dit onderzoeksproject dat zich richt op innovaties die rekening houden met een grotere rol van burgers bij een duurzame energievoorziening. Het project behelst echter meer dan het verrichten van onderzoek. Het beoogt bouwstenen te leveren voor een duurzame samenleving waarin meer ruimte is voor lokale (burger)initiatieven. We stellen drie deelprojecten voor:1. een vergelijkende studie naar energiecoöperaties en vergelijkbare innovatieve initiatieven, binnen en buiten Nederland, in heden en verleden. Daarbij hopen we lering te kunnen trekken uit de succesvolle ervaringen in Denemarken en Oostenrijk en van innovaties door coöperatiesen collectieven in het verleden.2. een analyse van energie-innovaties die beogen aan te sluiten bij lokale energiesystemen. Concreet zal het onderzoek zich richten op speciale batterijen, ontwikkeld dor het bedrijf Dr.Ten, en een soort slimme grote zoneboiler, ontwikkeld door het gelijknamige bedrijf Ecovat.3. De ontwikkeling van drie scenario’s, gebaseerd op inzichten uit studies 1 en 2. De scenario’s zullen bijvoorbeeld inhoudelijk verschillen in de mate waarin deze geïntegreerd zijn in bestaande energiesystemen. Deze zullen worden ontwikkeld en besproken met relevante stakeholders.Het onderzoek moet leiden tot een nauwkeurig overzicht van de mate van interesse en betrokkenheid van stakeholders en van de beperkingen en mogelijkheden van lokale energiesystemen en daarbij betrokken technologie. Ook leidt het tot een routemap voor duurzame energiesystemen op lokaal niveau. Het project heeft een technisch aspect, onderzoek naar verfijning en ontwikkeling van de technologie en een sociaal en normatief aspect, studies naar aansluitingsmogelijkheden bij de wensen en mogelijkheden van burgers, instanties en bedrijven in Noord-Nederland. Bovenal is het integratief en ontwerpend van karakter.This research proposal will explore new socio- technical configurations of local community-based sustainable energy systems. Energy collectives successfully combine technological and societal innovations, developing new business and organization models. A better understanding of their dynamics and needs will contribute to their continued success and thereby contribute to fulfilling the Top Sector’s Agenda. This work will also enhance the knowledge position of the Netherlands on this topic. Currently, over 500 local energy collectives are active in The Netherlands, many of them aim to produce their own sustainable energy, with thousands more in Europe. These collectives search for a new more local-based ways of organizing a sustainable society, including more direct democratic decision-making and influence on local living environment. The development of the collectives is enabled by openings in policy but –evenly important - by innovations in local energy production technologies (solar panels, windmills, biogas installations). Their future role in the sustainable energy transition can be strengthened by careful aligning new organizational and technological innovations in local energy production, storage and smart micro-grids.
