Recent years have shown the emergence of numerous local energy initiatives (prosumer communities) in the Netherlands. Many of them have set the goal to establish a local and sustainable energy provision on a not-for-profit basis. In this study we carried out exploratory case studies on a number of Dutch prosumer communities. The objective is to analyse their development process, to examine the barriers they encounter while organising their initiative, and to find how ICT could be applied to counteract these barriers and support communities in reaching their goals. The study shows that prosumer communities develop along a stepwise, evolutionary growth path, while they are struggling with organising their initiative, because the right expertise is lacking on various issues (such as energy technology, finance and legislation). Participants stated that, depending on the development phase of their initiative, there is a strong need for information and specific expertise. With a foreseeable growing technical complexity they indicated that they wanted to be relieved with the right tools and services at the right moment. Based on these findings we developed a generic solution through the concept of a prosumer community shopping mall. The concept provides an integrated and scalable ICT environment, offering a wide spectrum of energy services that supports prosumer communities in every phase of their evolutionary growth path. As such the mall operates as a broker and clearing house between 2 prosumer communities and service providers, where the service offerings grow and fit with the needs and demands of the communities along their growth path. The shopping mall operates for many prosumer communities, thus providing economies of scale. Each prosumer community is presented its own virtual mall, with specific content and a personalised look-and-feel.
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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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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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In the road transportation sector, CO2 emission target is set to reduce by at least 45% by 2030 as per the European Green Deal. Heavy Duty Vehicles contribute almost quarter of greenhouse gas emissions from road transport in Europe and drive majorly on fossil fuels. New emission restrictions creates a need for transition towards reduced emission targets. Also, increasing number of emission free zones within Europe, give rise to the need of hybridization within the truck and trailer community. Currently, in majority of the cases the trailer units do not possess any kind of drivetrain to support the truck. Trailers carry high loads, such that while accelerating, high power is needed. On the other hand, while braking the kinetic energy is lost, which otherwise could be recaptured. Thus, having a trailer with electric powertrain can support the truck during traction and can charge the battery during braking, helping in reducing the emissions and fuel consumption. Using the King-pin, the amount of support required by trailer can be determined, making it an independent trailer, thus requiring no modification on the truck. Given the heavy-duty environment in which the King-pin operates, the measurement design around it should be robust, compact and measure forces within certain accuracy level. Moreover, modification done to the King-pin is not apricated. These are also the challenges faced by V-Tron, a leading company in the field of services in mobility domain. The goal of this project is to design a smart King-pin, which is robust, compact and provides force component measurement within certain accuracy, to the independent e-trailer, without taking input from truck, and investigate the energy management system of the independent e-trailer to explore the charging options. As a result, this can help reduce the emissions and fuel consumption.
Door COVID-19 crisis zijn er extra uitdagingen om de verdere doorontwikkeling van het praktijkgerichte onderzoek en de onderliggende infrastructuur en professionalisering kwalitatief en kwantitatief te realiseren. De Hogeschool van Arnhem en Nijmegen (HAN) zet de IMPULS 2020 middelen in om de rol van het praktijkgericht onderzoek hierin te bestendigen en versterken. Het betreft een academie overstijgende aanvraag. Het beschikbare budget vanuit de regeling bedraagt 550.000 euro en zal in 2021 via twee lijnen worden ingezet: 1. Netwerk- en visievorming Dit richt zich op de versterking van de strategische netwerkvorming en samenhang overstijgend aan de zwaartepunten als focus gebieden voor de samenwerking onderwijs, onderzoek en werkveld (deels is hier aandacht voor de ontwikkeling en samenwerking bij regelingen als SPRONG of MMIP). Dit moet leiden tot het ontwikkelen van een meerjarige roadmap SLIM, SCHOON & SOCIAAL (S3). De regie ligt bij dit deel bij het zwaartepunt management. (Sustainable Energy & Environment (SEE), Smart Region en Health). 2. Professionalisering onderzoeksondersteuning Dit gedeelte betreft het vervolg op het project professionalisering onderzoeksondersteuning en richt zich (in lijn met het nationale project DCC) op de doorontwikkeling van: datastewardship, FAIR data & open access, ICT kennisinfrastructuur en communicatie rondom onderzoek en ondersteuning, verdere ontwikkeling van een Open Science Platform en voorbereiding op een HAN Open Access Fonds. Dit deel zal vanuit Services Onderwijs, Onderzoek en Kwaliteitszorg gecoördineerd worden. Middels deze inzet geeft de HAN een extra stimulans aan de strategische samenwerking en de verdere ontwikkeling van een consistente en herkenbare onderzoeksprogrammering en -ondersteuning.
