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.
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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?".
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Energy efficiency has gained a lot of prominence in recent debates on urban sustainability and housing policy due to its potential consequences for climate change. At the local, national and also international level, there are numerous initiatives to promote energy savings and the use of renewable energy to reduce the environmental burden. There is a lot of literature on energy saving and other forms of energy efficiency in housing. However, how to bring this forward in the management of individual housing organisations is not often internationally explored. An international research project has been carried out to find the answers on management questions of housing organisations regarding energy efficiency. Eleven countries have been included in this study: Germany, the United Kingdom (more specifically: England), France, Sweden, Denmark, the Netherlands, Switzerland, Slovenia, the Czech Republic, Austria and Canada. The state of the art of energy efficiency in the housing management of non-profit housing organisations and the embedding of energy efficiency to improve the quality and performance of housing in management practices have been investigated, with a focus on how policy ambitions about energy efficiency are brought forward in investment decisions at the estate level. This paper presents the conclusions of the research
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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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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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The energy management systems industry in the built environment is currently an important topic. Buildings use about 40% of the total global energy worldwide. Therefore, the energy management system’s sector is one of the most influential sectors to realize changes and transformation of energy use. New data science technologies used in building energy management systems might not only bring many technical challenges, but also they raise significant educational challenges for professionals who work in the field of energy management systems. Learning and educational issues are mainly due to the transformation of professional practices and networks, emerging technologies, and a big shift in how people work, communicate, and share their knowledge across the professional and academic sectors. In this study, we have investigated three different companies active in the building services sector to identify the main motivation and barriers to knowledge adoption, transfer, and exchange between different professionals in the energy management sector and explore the technologies that have been used in this field using the boundary-crossing framework. The results of our study show the importance of understanding professional learning networks in the building services sector. Additionally, the role of learning culture, incentive structure, and technologies behind the educational system of each organization are explained. Boundary-crossing helps to analyze the barriers and challenges in the educational setting and how new educational technologies can be embedded. Based on our results, future studies with a bigger sample and deeper analysis of technologies are needed to have a better understanding of current educational problems.
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A transition of today’s energy system towards renewableresources, requires solutions to match renewable energy generationwith demand over time. These solutions include smartgrids, demand-side management and energy storage. Energycan be stored during moments of overproduction of renewableenergy and used from the storage during moments ofinsufficient production. Allocation in real time of generatedenergy towards controlled appliances or storage chargers, isdone by a smart control system which makes decisions basedon predictions (of upcoming generation and demand) andinformation of the actual condition of storages.
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Uit de samenvatting: "Sinds medio 2017 is het Nationaal Lectorenplatform Urban Energy actief. De betrokken lectoren beogen het praktijkgericht onderzoek rond de gebouwde omgeving op hogescholen te verbinden en te stroomlijnen. Dit doen ze teneinde bij te dragen aan de energietransitie: met duurzame bronnen voorzien in onze energievoorziening. Een belangrijk instrument om de expertise van de lectoren te delen is een digitale onderzoekskaart, die beschikbaar is via: http://www.nlurbanenergy.nl. Daarnaast is er behoefte aan meer inzicht als het gaat om termen als vraagarticulatie en onderzoekssamenwerking. Meer precies wilden we achterhalen wat de behoefte is van het mkb aan praktijkgericht onderzoek van hogescholen in het domein Urban Energy. Daartoe hebben we een verkennende studie uitgevoerd naar praktijkgericht onderzoek binnen het domein Urban Energy. Hiervoor interviewden we de betrokken lectoren en ondernemers uit het innovatief MKB. Daarnaast maakten we gebruik van een enquête die we via verschillende kanalen onder de aandacht brachten bij het innovatief mkb."
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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.
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Key takeaways from the project underscore the importance of fostering long-term collaborations between technical experts, communities, and institutional partners. By integrating technical innovation with human-centred design, the SUSTENANCE project has not only advanced renewable energy adoption but also established a framework for empowering communities to actively participate in sustainable energy transitions. Moving forward, the lessons learned, and solutions developed provide a solid foundation for addressing future challenges in energy system decarbonization and resilience.
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