This article addresses European energy policy through conventional and transformative sustainability approaches. The reader is guided towards an understanding of different renewable energy options that are available on the policy making table and how the policy choices have been shaped. In arguing that so far, European energy policy has been guided by conventional sustainability framework that focuses on eco-efficiency and ‘energy mix’, this article proposes greater reliance on circular economy (CE) and Cradle to Cradle (C2C) frameworks. Exploring the current European reliance on biofuels as a source of renewable energy, this article will provide recommendations for transition to transformative energy choices. http://dx.doi.org/10.13135/2384-8677/2331 https://www.linkedin.com/in/helenkopnina/
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Energy policies are vital tools used by countries to regulate economic and social development as well as guarantee national security. To address the problems of fragmented policy objectives, conflicting tools, and overlapping initiatives, the internal logic and evolutionary trends of energy policies must be explored using the policy content. This study uses 38,277 energy policies as a database and summarizes the four energy policy objectives: clean, low-carbon, safe, and efficient. Using the TextCNN model to classify and deconstruct policies, the LDA + Word2vec theme conceptualization and similarity calculations were compared with the EISMD evolution framework to determine the energy policy theme evolution path. Results indicate that the density of energy policies has increased. Policies have become more comprehensive, barriers between objectives have gradually been broken, and low-carbon objectives have been strengthened. The evolution types are more diversified, evolution paths are more complicated, and the evolution types are often related to technology, industry, and market maturity. Traditional energy themes evolve through inheritance and merger; emerging technology and industry themes evolve through innovation, inheritance, and splitting. Moreover, this study provides a replicable analytical framework for the study of policy evolution in other sectors and evidence for optimizing energy policy design
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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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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 need to reduce carbon emissions calls for more use of renewable generation, particularly distributed resources. The intermittency of renewable generation, and concerns about energy security, require us to become more independent of central grid operation by use of local or regional (micro-grid) electricity systems. Distributed generation, allied to the commercial availability of battery storage products, permits this–the pathway to energy autonomy. This paper reviews the contribution of different renewable energy sources (RES), trends in energy storage technologies to enable energy autonomy, and the centralised and decentralised techniques that coordinate the associated energy management. The paper covers energy autonomy at different scales, ranging from household levels to district levels. The improvements in grid independency are measured accordingly. There is discussion of this measurement and of the economic and ecological benefits from energy autonomy in the context of policy frameworks.
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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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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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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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Global awareness on energy consumption and the environmental impacts of fossil fuels boost actions and create more supportive policies towards sustainable energy systems, in the last energy outlook, by the International Energy Agency, it was forecasted totals of 3600 GW from 2016 to 2040 of global deployment of renewables sources (RES), covering 37% of the power generation. While the Natural Gas overtake the coal demand in the energy mix, growing around 50%, manly by more efficiency system and the use of LNG for long-distance gas trades. The energy infrastructure will be more integrated, deploying decentralized and Hybrid Energy Networks (HEN).This transformation on the energy mix leads to new challenges for the energy system, related to the uncertainty and variability of RES, such as: Balancing flexibility, it means having sufficient resources to accommodate when variable production increase and load levels fall (or vice versa). And Efficiency in traditional fired plants, the often turn on and off or modify their output levels to accommodate changes in variable demand, can result in a decrease in efficiency, particularly from thermal stresses on equipment. This paper focus in the possibility to offer balancing resources from the LNG regasification, while ensure an efficient system.In order to asses this issue, using the energy Hub concept a model of a distributed HEN was developed. The HEN consist in a Waste to Energy plant (W2E), a more sustainable case of Combine Heat and Power (CHP) coupled with a LNG cold recovery regasification. To guarantee a most efficiency operation, the HEN was optimized to minimized the Exergy efficiency, additionally, the system is constrained by meeting Supply with variable demand, putting on evidence the sources of balancing flexibility. The case study show, the coupled system increases in overall exergy efficiency from 25% to 35% compared to uncoupled system; it brings additional energy between 1.75 and 3 MW, and it meets variable demand in the most exergy efficient with power from LNG reducing inputs of other energy carriers. All this indicated that LNG cold recovery in regasification coupled other energy systems is as promising tool to support the transition towards sustainable energy systems.
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This study explores how households interact with smart systems for energy usage, providing insights into the field's trends, themes and evolution through a bibliometric analysis of 547 relevant literature from 2015 to 2025. Our findings discover: (1) Research activity has grown over the past decade, with leading journals recognizing several productive authors. Increased collaboration and interdisciplinary work are expected to expand; (2) Key research hotspots, identified through keyword co-occurrence, with two (exploration and development) stages, highlighting the interplay between technological, economic, environmental, and behavioral factors within the field; (3) Future research should place greater emphasis on understanding how emerging technologies interact with human, with a deeper understanding of users. Beyond the individual perspective, social dimensions also demand investigation. Finally, research should also aim to support policy development. To conclude, this study contributes to a broader perspective of this topic and highlights directions for future research development.
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